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cosyBib2008.bib

@INPROCEEDINGS{brenner08aamas,
  AUTHOR = {Michael Brenner},
  TITLE = {Continual Collaborative Planning for Mixed-Initiative Action and
	Interaction},
  BOOKTITLE = {Proceedings of the 7th International Conference on Autonomous Agents
	and Multiagent Systems (AAMAS 2008)},
  YEAR = {2008},
  URL = {http://www.cognitivesystems.org/publications/brenner-aamas08.pdf}
}

@INPROCEEDINGS{Brenner/Kruijff:2008,
  AUTHOR = {Michael Brenner and Ivana Kruijff-Korbayova},
  TITLE = {A Continual Multiagent Planning Approach to Situated Dialogue},
  BOOKTITLE = {Proceedings of the 12th Workshop on the Semantics and Pragmatics
	of Dialogue (LonDial 2008)},
  YEAR = {2008},
  URL = {http://www.cognitivesystems.org/publications/brenner-kruijff-londial2008.pdf}
}

@ARTICLE{brenner08jaamas,
  AUTHOR = {Michael Brenner and Bernhard Nebel},
  TITLE = {Continual Planning and Acting in Dynamic Multiagent Environments},
  JOURNAL = {Journal of Autonomous Agents and Multiagent Systems},
  YEAR = {2008},
  URL = {http://www.cognitivesystems.org/publications/brenner-nebel06.pdf}
}

@INPROCEEDINGS{brenner08kr,
  AUTHOR = {Patrick Eyerich and Michael Brenner and Bernhard Nebel},
  TITLE = {On the Complexity of Planning Operator Subsumption},
  BOOKTITLE = {Proceedings of the Eleventh International Conference on Principles
	of Knowledge Representation and Reasoning (KR 2008)},
  YEAR = {2008},
  URL = {http://www.cognitivesystems.org/publications/eyerich-etal-kr2008.pdf}
}

@INPROCEEDINGS{fidler08cvpr,
  AUTHOR = {S. Fidler and B. Boben and A. Leonardis},
  TITLE = {Similarity-based cross-layered hierarchical representation for object
	categorization},
  BOOKTITLE = {IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
  YEAR = {2008},
  ADDRESS = {Alaska, USA},
  MONTH = {June},
  ABSTRACT = {This paper proposes a new concept in hierarchical representations
	that exploits features of different granularity and specificity coming
	from all layers of the hierarchy. The concept is realized within
	a cross-layered compositional representation learned from the visual
	data. We show how similarity connections among discrete labels within
	and across hierarchical layers can be established in order to produce
	a set of layer-independent shape-terminals, i.e. shapinals. We thus
	break the traditional notion of hierarchies and show how the category-specific
	layers can make use of all the necessary features stemming from all
	hierarchical layers. This, on the one hand, brings higher generalization
	into the representation, yet on the other hand, it also encodes the
	notion of scales directly into the hierarchy, thus enabling a multi-scale
	representation of object categories. By focusing on shape information
	only, the approach is tested on the Caltech 101 dataset demonstrating
	good performance in comparison with other state-of-the-art methods.},
  URL = {http://www.cognitivesystems.org/publications/fidler08cvpr.pdf}
}

@INPROCEEDINGS{Frintrop08a,
  AUTHOR = {Simone Frintrop and Patric Jensfelt},
  TITLE = {Active Gaze Control for Attentional Visual {SLAM}},
  BOOKTITLE = {Proceedings of the International Conference on Robotics and Automation
	(ICRA'08)},
  YEAR = {2008},
  ABSTRACT = {In this paper, we introduce an approach to active camera control for
	visual SLAM. Features, detected by a biologically motivated attention
	system, are tracked over several frames to determine stable landmarks.
	Matching of features to database entries enables global loop closing.
	The focus of this paper is the active camera control module, which
	supports the system with three behaviours: i) A tracking behaviour
	tracks promising landmarks and prevents them from leaving the field
	of view. ii) A redetection behaviour directs the camera actively
	to regions where landmarks are expected and thus supports loop closing.
	iii) Finally, an exploration behaviour investigates regions without
	landmarks and enables a more uniform distribution of landmarks. Several
	real-world experiments show that the active camera control outperforms
	the passive system considerably. },
  URL = {http://www.cognitivesystems.org/publications/frintropJensfelt_ICRA2008.pdf}
}

@INPROCEEDINGS{Frintrop08b,
  AUTHOR = {Simone Frintrop and Patric Jensfelt},
  TITLE = {Attentional Landmarks and Active Gaze Control for Visual {SLAM}},
  BOOKTITLE = {IEEE Transactions on Robotics, special Issue on Visual {SLAM}},
  YEAR = {2008},
  VOLUME = {24},
  MONTH = OCT,
  ABSTRACT = {This paper is centered around landmark detection, tracking and matching
	for visual SLAM (Simultaneous Localization And Mapping) using a monocular
	vision system with active gaze control. We present a system specialized
	in creating and maintaining a sparse set of landmarks based on a
	biologically motivated feature selection strategy. A visual attention
	system detects salient features which are highly discriminative,
	ideal candidates for visual landmarks which are easy to redetect.
	Features are tracked over several frames to determine stable landmarks
	and to estimate their 3D position in the environment. Matching of
	current landmarks to database entries enables loop closing. Active
	gaze control allows us to overcome some of the limitations of using
	a monocular vision system with a relatively small field of view.
	It supports (i) the tracking of landmarks which enable a better position
	estimation, (ii) the exploration of regions without landmarks to
	obtain a better distribution of landmarks in the environment, and
	(iii) the active redetection of landmarks to enable loop closing
	in situations in which a fixed camera fails to close the loop. Several
	real-world experiments show that accurate position estimation is
	obtained with the presented system and that active camera control
	outperforms the passive approach. },
  URL = {http://www.cognitivesystems.org/publications/frintropJensfeltTRO2008.pdf}
}

@INPROCEEDINGS{fritz08cvpr,
  AUTHOR = {Mario Fritz and Bernt Schiele},
  TITLE = {Decomposition, Discovery and Detection of Visual Categories Using
	Topic Models},
  BOOKTITLE = {Proceedings of CVPR},
  YEAR = {2008},
  MONTH = JUN,
  ABSTRACT = {We present a novel method for the discovery and detection of visual
	object categories based on decompositions using topic models. The
	approach is capable of learning a compact and low dimensional representation
	for multiple visual categories from multiple view points without
	labeling of the training instances. The learnt object components
	range from local structures over line segments to global silhouette-like
	descriptions. This representation can be used to discover object
	categories in a totally unsupervised fashion. Furthermore we employ
	the representation as the basis for building a supervised multi-category
	detection system making efficient use of training examples and outperforming
	pure features-based representations. The proposed speed-ups make
	the system scale to large databases. Experiments on three databases
	show that the approach improves the state-of-the-art in unsupervised
	learning as well as supervised detection. In particular we improve
	the state-of-the-art on the challenging PASCAL'06 multi-class detection
	tasks for several categories.},
  URL = {http://www.cognitivesystems.org/publications/cvpr08_mario.pdf}
}

@INPROCEEDINGS{Hawes/etal:2008a,
  AUTHOR = {Nick Hawes and Jeremy Wyatt},
  TITLE = {Benchmarking The Influence of Information-Processing Architectures
	on Intelligent Systems},
  BOOKTITLE = {Proceedings of the Robotics: Science \& Systems 2008 Workshop: Experimental
	Methodology and Benchmarking in Robotics Research},
  YEAR = {2008},
  MONTH = {June},
  ABSTRACT = {The design of the information-processing architecture used to develop
	an intelligent robot plays a significant role in the behaviour of
	the final system. In this paper we discuss the possibilities for
	benchmarking the influence of architecture designs on intelligent
	robots. We separate this problem into two sub-problems: benchmarking
	the architecture design and benchmarking the implementation of the
	design. For each of these sub-problems we list some design- and run-time
	properties that could be investigated. To further demonstrate these
	ideas we present some early efforts to benchmark the run-time properties
	of a previously developed architecture schema. },
  DATE-ADDED = {2009-01-04 20:30:15 +0000},
  DATE-MODIFIED = {2009-01-06 09:03:50 +0000},
  KEYWORDS = {cosy; irlab},
  LOCATION = {Zurich, Switzerland},
  URL = {http://www.cognitivesystems.org/publications/haweswyatt08benchmarking.pdf}
}

@INPROCEEDINGS{Hawes/etal:2008,
  AUTHOR = {Nick Hawes and Jeremy Wyatt and Aaron Sloman},
  TITLE = {Exploring Design Space For An Integrated Intelligent System},
  BOOKTITLE = {Research and Development in Intelligent Systems XXV: Proceedings
	of AI-2008, The Twenty-eighth SGAI International Conference on Innovative
	Techniques and Applications of Artificial Intelligence},
  YEAR = {2008},
  EDITOR = {Max Bramer and Frans Coenen and Miltos Petridis},
  ADDRESS = {Cambridge, England},
  MONTH = {December},
  PUBLISHER = {Springer},
  ABSTRACT = {Understanding the trade-offs available in the design space of intelligent
	systems is a major unaddressed element in the study of Artificial
	Intelligence. In this paper we approach this problem in two ways.
	First, we discuss the development of our integrated robotic system
	in terms of its trajectory through design space. Second, we demonstrate
	the practical implications of architectural design decisions by using
	this system as an experimental platform for comparing behaviourally
	similar yet architecturally different systems. The results of this
	show that our system occupies a "sweet spot" in design space in terms
	of the cost of moving information between processing components.},
  DATE-ADDED = {2009-01-04 20:37:48 +0000},
  DATE-MODIFIED = {2009-01-06 09:03:50 +0000},
  KEYWORDS = {cast; cosy; irlab},
  URL = {http://www.cognitivesystems.org/publications/hawesetal08ai.pdf}
}

@ARTICLE{Hawes/etal:2009,
  AUTHOR = {Nick Hawes and Jeremy Wyatt and Aaron Sloman},
  TITLE = {Exploring Design Space For An Integrated Intelligent System},
  JOURNAL = {Knowledge-Based Systems},
  YEAR = {2009},
  ABSTRACT = {Understanding the trade-offs available in the design space of intelligent
	systems is a major unaddressed element in the study of Artificial
	Intelligence. In this paper we approach this problem in two ways.
	First, we discuss the development of our integrated robotic system
	in terms of its trajectory through design space. Second, we demonstrate
	the practical implications of architectural design decisions by using
	this system as an experimental platform for comparing behaviourally
	similar yet architecturally different systems. The results of this
	show that our system occupies a "sweet spot" in design space in terms
	of the cost of moving information between processing components.},
  DATE-ADDED = {2009-01-05 12:57:16 +0000},
  DATE-MODIFIED = {2009-01-06 09:03:50 +0000},
  KEYWORDS = {cast; cosy; irlab},
  URL = {http://www.cognitivesystems.org/publications/hawesetal08kbs.pdf}
}

@INBOOK{hong08a,
  PAGES = {27-46},
  TITLE = {Learning Causality and Intentional Actions},
  PUBLISHER = {Springer},
  YEAR = {2008},
  AUTHOR = {S. Hongeng and J. Wyatt},
  SERIES = {LNAI: Towards Affordance-Based Robot Control},
  ABSTRACT = {Previous research has shown that human actions can be detected by
	motion patterns. However, labeling motion patterns is not sufficient
	in a cognitive system that requires reasoning about the agent's intentions,
	and how the environmental context affects the way an action is performed.
	In this paper, we develop a graphical model that captures how the
	movements that realize the action vary depending on the situ- ations,
	and present statistical learning algorithms. Using ob ject manip-
	ulation tasks, we illustrate how a system infers the agent's goals
	from visual observation and compare results with findings in psychological
	experiments.},
  URL = {http://www.cognitivesystems.org/publications/lnai07.pdf}
}

@INPROCEEDINGS{Jacobsson/etal:2008a,
  AUTHOR = {Jacobsson, H. and Hawes, N.A. and Kruijff, G.J.M. and Wyatt, J.},
  TITLE = {Crossmodal Content Binding in Information-Processing Architectures},
  BOOKTITLE = {Proceedings of the 3rd ACM/IEEE International Conference on Human-Robot
	Interaction (HRI)},
  YEAR = {2008},
  ADDRESS = {Amsterdam, The Netherlands},
  MONTH = {March},
  ABSTRACT = {Operating in a physical context, an intelligent robot faces two fundamental
	problems. First, it needs to combine information from its different
	sensors to form a representation of the environment that is more
	complete than any representation a single sensor could provide. Second,
	it needs to combine high-level representations (such as those for
	planning and dialogue) with sensory information, to ensure that the
	interpretations of these symbolic representations are grounded in
	the situated context. Previous approaches to this problem have used
	techniques such as (low-level) information fusion, ontological reasoning,
	and (high-level) concept learning. This paper presents a framework
	in which these, and related approaches, can be used to form a shared
	representation of the current state of the robot in relation to its
	environment and other agents. Preliminary results from an implemented
	system are presented to illustrate how the framework supports behaviours
	commonly required of an intelligent robot.},
  URL = {http://www.cognitivesystems.org/publications/hri_binding.pdf}
}

@ARTICLE{Kristan2009,
  AUTHOR = {Kristan, M. and Per\v{s}, J. and Kova\v{c}i\v{c}, S. and Leonardis,
	A.},
  TITLE = {A Local-motion-based probabilistic model for visual tracking},
  JOURNAL = {Pattern Recognition},
  YEAR = {2008},
  NOTE = {accepted for publication},
  ABSTRACT = {Color-based tracking is prone to failure in situations where visually
	similar targets are moving in a close proximity or occlude each other.
	To deal with the ambiguities in the visual information, we propose
	an additional color-independent visual model based on the target's
	local motion. This model is calculated from the optical flow induced
	by the target in consecutive images. By modifying a color-based particle
	filter to account for the target's local motion, the combined color/local-motion-based
	tracker is constructed. We compare the combined tracker to a purely
	color-based tracker on a challenging dataset from hand tracking,
	surveillance and sports. The experiments show that the proposed local-motion
	model largely resolves situations when the target is occluded by,
	or moves in front of, a visually similar object.},
  URL = {http://www.cognitivesystems.org/publications/MatejKristanPR09.pdf}
}

@INPROCEEDINGS{kristanCVWW08,
  AUTHOR = {M. Kristan and D. Sko\v{c}aj and A. Leonardis},
  TITLE = {Incremental learning with {G}aussian mixture models},
  BOOKTITLE = {Computer Vision Winter Workshop CVWW 2008},
  YEAR = {2008},
  PAGES = {25-32},
  ADDRESS = {Moravske toplice, Slovenia},
  MONTH = {February},
  ABSTRACT = {In this paper we propose a new incremental estimation of Gaussian
	mixture models which can be used for applications of online learning.
	Our approach allows for adding new samples incrementally as well
	as removing parts of the mixture by the process of unlearning. Low
	complexity of the mixtures is maintained through a novel compression
	algorithm. In contrast to the existing approaches, our approach does
	not require fine-tuning parameters for a specific application, we
	do not assume specific forms of the target distributions and temporal
	constraints are not assumed on the observed data. The strength of
	the proposed approach is demonstrated with an example of online estimation
	of a complex distribution, an example of unlearning, and with an
	interactive learning of basic visual concepts.},
  URL = {http://www.cognitivesystems.org/publications/kristanCVWW08.pdf}
}

@ARTICLE{KristanIMAVIS2008,
  AUTHOR = {Kristan, M. and Sko\v{c}aj, D. and Leonardis, A.},
  TITLE = {Online Kernel Density Estimation For Interactive Learning},
  JOURNAL = {Image and Vision Computing},
  YEAR = {2008},
  ABSTRACT = {In this paper we propose a Gaussian-kernel-based online kernel density
	estimation which can be used for applications of online probability
	density estimation and online learning. Our approach generates a
	Gaussian mixture model of the observed data and allows online adaptation
	from positive examples as well as from the negative examples. The
	adaptation from the negative examples is realized by a novel concept
	of unlearning in mixture models. Low complexity of the mixtures is
	maintained through a novel compression algorithm. In contrast to
	the existing approaches, our approach does not require fine-tuning
	parameters for a specific application, we do not assume specific
	forms of the target distributions and temporal constraints are not
	assumed on the observed data. The strength of the proposed approach
	is demonstrated with examples of online estimation of complex distributions,
	an example of unlearning, and with an interactive learning of basic
	visual concepts.},
  COMMENT = {submitted for publication}
}

@INPROCEEDINGS{Kruijff/etal:2008,
  AUTHOR = {G.J.M. Kruijff and M. Brenner and N.A. Hawes},
  TITLE = {Continual Planning for Cross-Modal Situated Clarification in Human-Robot
	Interaction},
  BOOKTITLE = {Proceedings of the 17th International Symposium on Robot and Human
	Interactive Communication (RO-MAN 2008)},
  YEAR = {2008},
  ADDRESS = {Munich, Germany},
  MONTH = {August},
  ABSTRACT = {Robots do not fully understand the world they are situated in. This
	includes what humans talk to them about. A fundamental problem is
	thus how a robot can clarify such a lack of understanding. This paper
	addresses the issue of how a robot can create a plan for resolving
	a need for clarification. It characterises situated clarification
	as an information need which may arise in any sensory-motoric modality
	required to interpret the situated context of the robot, or any deliberative
	modality referring to that context. It then focuses on how, once
	a clarification need has been identified, the robot can create a
	plan in which one or more modalities are used to resolve it. Modalities
	are involved on the basis of the types of information they can provide.
	These information types are identified in the ontologies the modalities
	use to interconnect their content with content of other modalities
	(via information fusion). We take a continual approach to planning
	and execution monitoring. This provides the ability to re-plan depending
	on modality availability and success in resolving (part of) a clarification
	need. We illustrate the implementation on several examples.},
  URL = {http://www.cognitivesystems.org/publications/main.sitclar.roman2008.pdf}
}

@BOOK{Kruijff/etal:2008-ICRA,
  TITLE = {Proceedings of the ICRA 2008 Workshop: Social Interaction with Intelligent
	Indoor Robots (SI3R)},
  PUBLISHER = {ICRA},
  YEAR = {2008},
  AUTHOR = {G.J.M. Kruijff and H. Zender and M. Hanheide and B. Wrede},
  ADDRESS = {Pasadena, CA, USA},
  MONTH = {May},
  ABSTRACT = {Robots are moving from the factories into our homes. Today, we have
	the Roomba. Tomorrow, in 2010, industry aims to give us the first
	commercial humanoids. Bringing robots as assistants into homes, offices,
	and shopping malls presents serious challenges to human-robot interaction.
	Robots will need to assist untrained users. Robots will need to interact
	with people in environments that are designed for, and populated
	by, humans. This workshop focuses on how robotic systems can be designed
	such as to meet these challenges. Make robots adapt to the environment.
	Make robots socially acceptable. Make robots fit into the environment,
	without the environment needing to be made to fit them.},
  URL = {http://www.dfki.de/cosy/www/events/si3r-icra08/}
}

@INPROCEEDINGS{Galvez08a,
  AUTHOR = {Dorian G\'alvez L\'opez and Kristoffer Sj\"{o} and Chandana Paul
	and Patric Jensfelt},
  TITLE = {Hybrid Laser and Vision Based Object Search and Localization},
  BOOKTITLE = {Proceedings of the International Conference on Robotics and Automation
	(ICRA'08)},
  YEAR = {2008},
  ABSTRACT = {We describe a method for an autonomous robot to efficiently locate
	one or more distinct objects in a realistic environment using monocular
	vision. We demonstra te how to efficiently subdivide acquired images
	into interest regions for the robot to zoom in on, using receptive
	field cooccurrence histograms. Objects are recognized through SIFT
	feature matching and the positions of the objects are es timated.
	Assuming a 2D map of the robot's surroundings and a set of navigation
	nodes betw een which it is free to move, we show how to compute an
	efficient sensing plan that allows the robot's camera to cover the
	environment, while obeying restrictions on the different objects'
	maximum and minimum viewing distances. The approach has been implemented
	on a real robotic system and results are prese nted showing its practicability
	and the quality of the position estimates obtained.},
  URL = {http://www.cognitivesystems.org/publications/galvezetal-icra08.pdf}
}

@ARTICLE{leibe08ivc,
  AUTHOR = {Bastian Leibe and Alan Ettlin and Bernt Schiele},
  TITLE = {Learning semantic object parts for object categorization},
  JOURNAL = {Image Vision Comput.},
  YEAR = {2008},
  VOLUME = {26},
  PAGES = {15--26},
  NUMBER = {1},
  ABSTRACT = {ppearance-based approaches to object recognition mostly rely on measuring
	the visual similarity of objects based on global or local descriptors.
	They have shown great success in object identification but often
	do not generalize to the more challenging case of object categorization,
	where category membership is often decided not only on a level of
	appearances, but also on a semantic level. It has been argued that
	model-based approaches are better suited to this problem, since they
	allow to inject high-level knowledge, for example about the constituting
	object parts and possible configurations. Postulating a set of object
	parts is problematic, though, since it is not guaranteed that those
	parts can be reliably extracted from real-world images. There is
	a need for a middle layer, forming an interface between the visual
	information readily available from the image and the higher-level
	semantic information that can be used by reasoning processes. In
	this work, we investigate how such an interface can be learned. As
	the appearance of object parts may vary considerably, this cannot
	be achieved by relying on visual similarity alone. Rather, this paper
	proposes to also use co-location and co-activation, together with
	weak top-down constraints, such as alignment, as guiding principles
	for learning the appearance of local object parts. The learned structures
	generalize beyond the appearance of single objects and often correspond
	to semantically plausible object parts, such as wheels, trunks, or
	windshields of cars. In a later stage, a Bayesian network of those
	extracted structures is used to verify object hypotheses successfully
	in difficult scenes.},
  ADDRESS = {Newton, MA, USA},
  DOI = {http://dx.doi.org/10.1016/j.imavis.2007.08.012},
  ISSN = {0262-8856},
  PUBLISHER = {Butterworth-Heinemann}
}

@ARTICLE{Leibe05c,
  AUTHOR = {B. Leibe and A. Leonardis and B. Schiele},
  TITLE = {Robust Object Detection with Interleaved Categorization and Segmentation
	
	},
  JOURNAL = {International Journal of Computer Vision},
  YEAR = {2008},
  PAGES = {259--289},
  NUMBER = {1--3},
  ABSTRACT = {This paper presents a novel method for detecting and localizing objects
	of a visual category in cluttered real-world scenes. Our approach
	considers object categorization and figure-ground segmentation as
	two interleaved processes that closely collaborate towards a common
	goal. As shown in our work, the tight coupling between those two
	processes allows them to benefit from each other and improve the
	combined performance. The core part of our approach is a highly flexible
	learned representation for object shape that can combine the information
	observed on different training examples in a probabilistic extension
	of the Generalized Hough Transform. The resulting approach can detect
	categorical objects in novel images and automatically infer a probabilistic
	segmentation from the recognition result. This segmentation is then
	in turn used to again improve recognition by allowing the system
	to focus its efforts on object pixels and to discard misleading influences
	from the background. Moreover, the information from where in the
	image a hypothesis draws its support is employed in an MDL based
	hypothesis verification stage to resolve ambiguities between overlapping
	hypotheses and factor out the effects of partial occlusion.},
  URL = {http://www.cognitivesystems.org/publications/fulltext.pdf},
  VOL = {77}
}

@INPROCEEDINGS{Lison:2008,
  AUTHOR = {P. Lison},
  TITLE = {A salience-driven approach to speech recognition for human-robot
	interaction},
  BOOKTITLE = {Proceedings of the ESSLLI 2008 Student Session},
  YEAR = {2008},
  ADDRESS = {Hamburg, Germany},
  MONTH = {August},
  ABSTRACT = {We present an implemented model for speech recognition in natural
	en- vironments which relies on contextual information about salient
	entities to prime utterance recognition. The hypothesis underlying
	our approach is that, in situated human-robot interaction, speech
	recognition performance can be significantly en- hanced by exploiting
	knowledge about the immediate physical environment and the dialogue
	history. To this end, visual salience (objects perceived in the physical
	scene) and linguistic salience (previously referred-to objects within
	the current dialogue) are integrated into a single cross-modal salience
	model. The model is dynamically up- dated as the environment evolves,
	and is used to establish expectations about uttered words which are
	most likely to be heard given the context. The update is realised
	by continously adapting the word-class probabilities specified in
	the statistical language model. The present article discusses the
	motivations behind our approach, describes our implementation as
	part of a distributed, cognitive architecture for mobile robots,
	and reports the evaluation results on a test suite.},
  URL = {http://www.cognitivesystems.org/publications/situatedASR-ESSLLI08.pdf}
}

@INPROCEEDINGS{Lison/Kruijff:2008,
  AUTHOR = {P. Lison and G.J.M. Kruijff.},
  TITLE = {Salience-driven Contextual Priming of Speech Recognition for Human-Robot
	Interaction},
  BOOKTITLE = {Proceedings of ECAI 2008},
  YEAR = {2008},
  ADDRESS = {Athens, Greece},
  MONTH = {July},
  ABSTRACT = {The paper presents an implemented model for priming speech recognition,
	using contextual information about salient entities. The underlying
	hypothesis is that, in human-robot interaction, speech recognition
	performance can be improved by exploiting knowledge about the immediate
	physical situation and the dialogue history. To this end, visual
	salience (objects perceived in the physical scene) and linguistic
	salience (objects, events already mentioned in the dialogue) are
	integrated into a single cross-modal salience model. The model is
	dynamically updated as the environment changes. It is used to establish
	expectations about which words are most likely to be heard in the
	given context. The update is realised by continuously adapting the
	word-class probabilities specified in a statistical language model.
	The paper discusses the motivations behind the approach, and presents
	the implementation as part of a cognitive architecture for mobile
	robots. Evaluation results on a test suite show a statistically significant
	improvement of salience-driven priming speech recognition (WER) over
	a commercial baseline system.},
  URL = {http://www.cognitivesystems.org/publications/main.sitASR.ecai08.pdf}
}

@PHDTHESIS{mozos2008phd,
  AUTHOR = {Oscar Martinez Mozos},
  TITLE = {Semantic Place Labeling with Mobile Robots},
  SCHOOL = {University of Freiburg},
  YEAR = {2008},
  ADDRESS = {Freiburg, Germany},
  MONTH = {July},
  FILE = {phd_thesis.pdf:http\://www.informatik.uni-freiburg.de/~omartine/publications/phd_thesis.pdf:PDF}
}

@INPROCEEDINGS{plagemann08ecml,
  AUTHOR = {Plagemann, C. and Kersting, K. and Burgard, W.},
  TITLE = {Nonstationary Gaussian Process Regression using Point Estimates of
	Local Smoothness},
  BOOKTITLE = {Proc.~of the European Conference on Machine Learning (ECML)},
  YEAR = {2008},
  ADDRESS = {Antwerp, Belgium},
  ABSTRACT = {Gaussian processes using nonstationary covariance functions are a
	powerful tool for Bayesian regression with input-dependent smoothness.
	A common approach is to model the local smoothness by a latent process
	that is integrated over using Markov chain Monte Carlo approaches.
	In this paper, we demonstrate that an approximation that uses the
	estimated mean of the local smoothness yields good results and allows
	one to employ efficient gradient-based optimization techniques for
	jointly learning the parameters of the latent and the observed processes.
	Extensive experiments on both synthetic and real-world data, including
	challenging problems in robotics, show the relevance and feasibility
	of our approach.},
  URL = {http://www.cognitivesystems.org/publications/plagemann08ecml.pdf}
}

@INPROCEEDINGS{plagemann08iros,
  AUTHOR = {Plagemann, C. and Mischke, S. and Prentice, S. and Kersting, K. and
	Roy, N. and Burgard, W.},
  TITLE = {Learning Predictive Terrain Models for Legged Robot Locomotion},
  BOOKTITLE = {Proc.~of the IEEE/RSJ International Conference on Intelligent Robots
	and Systems (IROS)},
  YEAR = {2008},
  ADDRESS = {Nice, France},
  ABSTRACT = {Legged robots require the ability to build accurate models of their
	environment in order to plan and execute their actions. We present
	a novel, probabilistic terrain model based on Gaussian processes
	that can be learned and updated efficiently using sparse approximation
	techniques. The major benefit of our model is its ability to predict
	elevations at unseen locations more reliably than alternative approaches,
	while it also yields estimates of the predictive uncertainties. In
	particular, our Gaussian process adapts its covariance to the situation
	at hand, allowing more accurate inference of terrain height at points
	that have not been directly observed. We show how a conventional
	motion planner can use the learned terrain model to to plan a path
	to a goal location, using a terrain-specific cost model to accept
	or reject candidate footholds. In experiments with a real quadruped
	robot equipped with a laser range finder, we demonstrate the usefulness
	of our approach and discuss its benefits compared to simpler terrain
	models such as elevations grids.},
  URL = {http://www.cognitivesystems.org/publications/plagemann08iros.pdf}
}

@INPROCEEDINGS{pronobis08icra,
  AUTHOR = {Pronobis, A. and Mart\'{i}nez Mozos, O. and Caputo, B.},
  TITLE = {{SVM}-based Discriminative Accumulation Scheme for Place Recognition},
  BOOKTITLE = {Proceedings of the IEEE International Conference on Robotics and
	Automation (ICRA'08)},
  YEAR = {2008},
  ADDRESS = {Pasadena, CA, USA},
  MONTH = {May},
  ABSTRACT = {Integrating information coming from different sensors is a fundamental
	capability for autonomous robots. For complex tasks like topological
	localization, it would be desirable to use multiple cues, possibly
	from different modalities, so to achieve robust performance. This
	paper proposes a new method for integrating multiple cues. For each
	cue we train a large margin classifier which outputs a set of scores
	indicating the confidence of the decision. These scores are then
	used as input to a Support Vector Machine, that learns how to weight
	each cue, for each class, optimally during training. We call this
	algorithm SVM-based Discriminative Accumulation Scheme (SVM-DAS).
	We applied our method to the topological localization task, using
	vision and laser-based cues. Experimental results clearly show the
	value of our approach.},
  URL = {http://www.cognitivesystems.org/publications/pronobis08icra.pdf}
}

@INPROCEEDINGS{ridgeCogSys08,
  AUTHOR = {B. Ridge and D. Sko\v{c}aj, and A. Leonardis},
  TITLE = {A System for Learning Basic Object Affordances using a Self-Organizing
	Map},
  BOOKTITLE = {International Conference on Cognitive Systems CogSys 2008},
  YEAR = {2008},
  ADDRESS = {Karlsruhe, Germany},
  ABSTRACT = {When a cognitive system encounters particular objects, it needs to
	know what effect each of its possible actions will have on the state
	of each of those objects in order to be able to make effective decisions
	and achieve its goals. Moreover, it should be able to generalize
	effectively so that when it encounters novel objects, it is able
	to estimate what effect its actions will have on them based on its
	experiences with previously encountered similar objects. This idea
	is encapsulated by the term “affordance”, e.g. “a ball affords being
	rolled to the right when pushed from the left.” In this paper, we
	discuss the development of a cognitive vision platform that uses
	a robotic arm to interact with household objects in an attempt to
	learn some of their basic affordance properties. We outline the various
	sensor and effector module competencies that were needed to achieve
	this and describe an experiment that uses a self-organizing map to
	integrate these modalities in a working affordance learning system.},
  URL = {http://www.cognitivesystems.org/publications/ridgeCogSys08.pdf}
}

@INPROCEEDINGS{ridgeEpiRob08,
  AUTHOR = {Ridge, B. and Sko\v{c}aj, D and Leonardis, A},
  TITLE = {Towards Learning Basic Object Affordances from Object Properties},
  BOOKTITLE = {Proceedings of Eight International Conference on Epigenetic Robotics},
  YEAR = {2008},
  ABSTRACT = {The capacity for learning to recognize and exploit environmental affordances
	is an important consideration for the design of current and future
	developmental robotic systems. We present a system that uses a robotic
	arm, camera systems and self-organizing maps to learn basic affordances
	of objects.},
  BIBTEX_AGE = {6},
  BIBTEX_DATE = {2008-07-30},
  URL = {http://www.cognitivesystems.org/publications/ridgeEpiRob08.pdf}
}

@INPROCEEDINGS{schnitzspan08eccv,
  AUTHOR = {Paul Schnitzspan and Mario Fritz and Bernt Schiele},
  TITLE = {Hierarchical Support Vector Random Fields: Joint Training to Combine
	Local and Global Features},
  BOOKTITLE = {European Conference on Computer Vision (ECCV)},
  YEAR = {2008},
  ADDRESS = {Marseille, France},
  ABSTRACT = {Recently, impressive results have been reported for the de- tection
	of ob jects in challenging real-world scenes. Interestingly however,
	the underlying models vary greatly even between the most successful
	ap- proaches. Methods using a global feature descriptor (e.g. [1])
	paired with discriminative classi?ers such as SVMs enable high levels
	of performance, but require large amounts of training data and typically
	degrade in the presence of partial occlusions. Local feature-based
	approaches (e.g. [2–4]) are more robust in the presence of partial
	occlusions but often produce a signi?cant number of false positives.
	This paper proposes a novel ap- proach called hierarchical support
	vector random ?eld that allows 1) to combine the power of global
	feature-based approaches with the ?exibility of local feature-based
	methods in one consistent multi-layer framework and 2) to automatically
	learn the tradeo? and the optimal interplay between local, semi-local
	and global feature contributions. Experiments show that both the
	combination of local and global features as well as the joint training
	result in improved detection performance on challenging datasets.},
  URL = {http://www.cognitivesystems.org/publications/hsvrf.pdf}
}

@INPROCEEDINGS{Sjoe08b,
  AUTHOR = {Kristoffer Sj\"o and Chandana Paul},
  TITLE = {Object Localization using Bearing Only Visual Detection},
  BOOKTITLE = {Proceedings of the 10th International Conference on Intelligent Autonomous
	Systems},
  YEAR = {2008},
  MONTH = {july},
  ABSTRACT = {This work demonstrates how an autonomous robotic platform can use
	intrinsically noisy, coarse-scale visual methods lacking range information
	to produce good estimates of the location of objects, by using a
	map space representation for weighting together multiple observations
	from different vantage points. As the robot moves through the environment
	it acquires visual images which are processed by means of a fast
	but noisy visual detection algorithm that gives bearing only information.
	The results from the detection are then projected from image space
	into map space, where data from multiple viewpoints can intrinsically
	combine to yield an increasingly accurate picture of the location
	of objects. This method has been implemented and shown to work for
	object localization on a real robot. It has also been tested extensively
	in simulation, with systematically varied false positive and false
	negative detection rates. The results demonstrate that this is a
	viable method for object localization, even under a wide range of
	sensor uncertainties.},
  URL = {http://www.cognitivesystems.org/publications/SjooIAS08.pdf}
}

@INPROCEEDINGS{skocajVISAPP08,
  AUTHOR = {D. Sko\v{c}aj and M. Kristan and A. Leonardis},
  TITLE = {Continuous Learning of Simple Visual Concepts Using Incremental Kernel
	Density Estimation},
  BOOKTITLE = {International Conference on Computer Vision Theory and Applications},
  YEAR = {2008},
  PAGES = {598-604},
  ADDRESS = {Funchal, Madeira, Portugal},
  MONTH = {January},
  ABSTRACT = {In this paper we propose a method for continuous learning of simple
	visual concepts. The method continuously associates words describing
	observed scenes with automatically extracted visual features. Since
	in our setting every sample is labelled with multiple concept labels,
	and there are no negative examples, reconstructive representations
	of the incoming data are used. The associated features are modelled
	with kernel density probability distribution estimates, which are
	built incrementally. The proposed approach is applied to the learning
	of object properties and spatial relations.},
  URL = {http://www.cognitivesystems.org/publications/skocajVISAPP08.pdf}
}

@ARTICLE{skocajIMAVIS08,
  AUTHOR = {D. Sko\v{c}aj and A. Leonardis},
  TITLE = {Incremental and robust learning of subspace representations},
  JOURNAL = {Image vis. comput.},
  YEAR = {2008},
  VOLUME = {26},
  PAGES = {27-38},
  NUMBER = {1},
  ABSTRACT = {Learning is a fundamental capability of any cognitive system. To enable
	efficient operation of a cognitive agent in a real-world environment,
	visual learning has to be a continuous and robust process. In this
	article, we present a method for subspace learning, which takes these
	considerations into account. We present an incremental method, which
	sequentially updates the principal subspace considering weighted
	influence of individual images as well as individual pixels within
	an image. We further extend this approach to enable determination
	of consistencies in the input data and imputation of the inconsistent
	values using the previously acquired knowledge, resulting in a novel
	method for incremental, weighted, and robust subspace learning. We
	demonstrate the effectiveness of the proposed concept in several
	experiments on learning of object and background representations.},
  URL = {http://www.cognitivesystems.org/publications/skocajIMAVIS08.pdf}
}

@INPROCEEDINGS{Sloman:2008c,
  AUTHOR = {Aaron Sloman},
  TITLE = {{Kantian Philosophy of Mathematics and Young Robots}},
  BOOKTITLE = {{Intelligent Computer Mathematics}},
  YEAR = {2008},
  EDITOR = {Autexier, S. and Campbell, J. and Rubio, J. and Sorge, V. and Suzuki,
	M. and Wiedijk, F.},
  SERIES = {LLNCS no 5144},
  PAGES = {558-573},
  ADDRESS = {Berlin/Heidelberg},
  MONTH = {July},
  PUBLISHER = {Springer},
  NOTE = {http://www.cs.bham.ac.uk/research/projects/cosy/papers\#tr0802},
  ABSTRACT = {A child, or young human-like robot of the future, needs to develop
	an information-processing architecture, forms of representation,
	and mechanisms to support perceiving, manipulating, and thinking
	about the world, especially perceiving and thinking about actual
	and possible structures and processes in a 3-D environment. The mechanisms
	for extending those representations and mechanisms, are also the
	core mechanisms required for developing mathematical competences,
	especially geometric and topological reasoning competences. Understanding
	both the natural processes and the requirements for future human-like
	robots requires AI designers to develop new forms of representation
	and mechanisms for geometric and topological reasoning to explain
	a child's (or robot's) development of understanding of affordances,
	and the proto-affordances that underlie them. A suitable multi-functional
	self-extending architecture will enable those competences to be developed.
	Within such a machine, human-like mathematical learning will be possible.
	It is argued that this can support Kant's philosophy of mathematics,
	as against Humean philosophies. It also exposes serious limitations
	in studies of mathematical development by psychologists.},
  DATE-ADDED = {2009-01-04 19:55:40 +0000},
  DATE-MODIFIED = {2009-01-06 09:03:50 +0000},
  INSTITUTION = {School of Computer Science, University of Birmingham},
  KEYWORDS = {cosy; irlab},
  URL = {http://www.cognitivesystems.org/publications/maths-ai-sloman.pdf}
}

@INPROCEEDINGS{Sloman/etal:2008b,
  AUTHOR = {Aaron Sloman},
  TITLE = {Architectural and Representational Requirements for Seeing Processes,
	Proto-affordances and Affordances},
  BOOKTITLE = {Logic and Probability for Scene Interpretation },
  YEAR = {2008},
  EDITOR = {Anthony G. Cohn and David C. Hogg and Ralf M{\"o}ller and Bernd Neumann},
  NUMBER = {08091},
  SERIES = {Dagstuhl Seminar Proceedings},
  ADDRESS = {Dagstuhl, Germany},
  PUBLISHER = {Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, Germany},
  ABSTRACT = {This paper, combining the standpoints of philosophy and Artificial
	Intelligence with theoretical psychology, summarises several decades
	of investigation by the author of the variety of functions of vision
	in humans and other animals, pointing out that biological evolution
	has solved many more problems than are normally noticed. For example,
	the biological functions of human and animal vision are closely related
	to the ability of humans to do mathematics, including discovering
	and proving theorems in geometry, topology and arithmetic. Many of
	the phenomena discovered by psychologists and neuroscientists require
	sophisticated controlled laboratory settings and specialised measuring
	equipment, whereas the functions of vision reported here mostly require
	only careful attention to a wide range of everyday competences that
	easily go unnoticed. Currently available computer models and neural
	theories are very far from explaining those functions, so progress
	in explaining how vision works is more in need of new proposals for
	explanatory mechanisms than new laboratory data. Systematically formulating
	the requirements for such mechanisms is not easy. If we start by
	analysing familiar competences, that can suggest new experiments
	to clarify precise forms of these competences, how they develop within
	individuals, which other species have them, and how performance varies
	according to conditions. This will help to constrain requirements
	for models purporting to explain how the competences work. For example,
	Gibson's theory of affordances needs a number of extensions, including
	allowing affordances to be composed in several ways from lower level
	proto-affordances. The paper ends with speculations regarding the
	need for new kinds of information-processing machinery to account
	for the phenomena. },
  ANNOTE = {Keywords: Vision, affordances, architectures, development, design
	space},
  DATE-ADDED = {2009-01-04 19:55:40 +0000},
  DATE-MODIFIED = {2009-01-06 09:03:50 +0000},
  ISSN = {1862-4405},
  KEYWORDS = {cosy; irlab},
  URL = {http://www.cognitivesystems.org/publications/08091.SlomanAaron.Paper.1656.pdf}
}

@INPROCEEDINGS{Sloman:2008d,
  AUTHOR = {Aaron Sloman},
  TITLE = {{Varieties of Meta-cognition in Natural and Artificial Systems}},
  BOOKTITLE = {{Workshop on Metareasoning, AAAI'08 Conference}},
  YEAR = {2008},
  EDITOR = {M. T. Cox and A. Raja},
  PAGES = {12--20},
  ADDRESS = {Menlo Park, CA},
  PUBLISHER = {AAAI Press},
  NOTE = {http://www.cs.bham.ac.uk/research/projects/cosy/papers/\#tr0803},
  ABSTRACT = {Some AI researchers aim to make useful machines, including robots.
	Others aim to understand general principles of information-processing
	machines whether natural or artificial, often with special emphasis
	on humans and human-like systems: They primarily address scientific
	and philosophical questions rather than practical goals. However,
	the tasks required to pursue scientific and engineering goals overlap
	considerably, since both involve building working systems to test
	ideas and demonstrate results, and the conceptual frameworks and
	development tools needed for both overlap. This paper, partly based
	on requirements analysis in the CoSy robotics project, surveys varieties
	of meta-cognition and draws attention to some types that appear to
	play a role in intelligent biological individuals (e.g. humans) and
	which could also help with practical engineering goals, but seem
	not to have been noticed by most researchers in the field. There
	are important implications for architectures and representations.
	},
  DATE-ADDED = {2009-01-04 19:55:40 +0000},
  DATE-MODIFIED = {2009-01-06 09:03:50 +0000},
  KEYWORDS = {cosy; irlab},
  URL = {http://www.cognitivesystems.org/publications/sloman-meta-aaai08.pdf}
}

@INCOLLECTION{Sloman:2009a,
  AUTHOR = {Aaron Sloman},
  TITLE = {{Machines in the Ghost}},
  BOOKTITLE = {{Simulating the Mind: A Technical Neuropsychoanalytical Approach}},
  PUBLISHER = {Springer},
  YEAR = {2009},
  EDITOR = {Dietrich, D. and Fodor, G. and Zucker, G. and Bruckner, D.},
  PAGES = {124--148},
  ADDRESS = {Vienna \& New York},
  NOTE = {http://www.cs.bham.ac.uk/research/projects/cosy/papers/\#tr0702},
  ABSTRACT = {This paper summarises ideas I have been working on over the last 35
	years or so, about relations between the study of natural minds and
	the design of artificial minds, and the requirements for both sorts
	of minds. The key idea is that natural minds are information-processing
	virtual machines produced by evolution. What sort of information-processing
	machine a human mind is requires much detailed investigation of the
	many kinds of things minds can do. At present, it is not clear whether
	producing artificial minds with similar powers will require new kinds
	of computing machinery or merely much faster and bigger computers
	than we have now. Some things once thought hard to implement in artificial
	minds, such as affective states and processes, including emotions,
	can be construed as aspects of the control mechanisms of minds. This
	view of mind is largely compatible in principle with psychoanalytic
	theory, though some details are very different. The therapeutic aspect
	of psychoanalysis is analogous to run-time debugging of a virtual
	machine. In order to do psychotherapy well we need to understand
	the architecture of the machine well enough to know what sorts of
	bugs can develop and which ones can be removed, or have their impact
	reduced, and how. Otherwise treatment will be a hit-and-miss affair.
	},
  DATE-ADDED = {2009-01-04 19:55:40 +0000},
  DATE-MODIFIED = {2009-01-06 09:03:50 +0000},
  KEYWORDS = {cosy; irlab},
  URL = {http://www.cognitivesystems.org/publications/sloman-enf07.pdf}
}

@INCOLLECTION{Sloman:2009b,
  AUTHOR = {Aaron Sloman},
  TITLE = {{Some Requirements for Human-like Robots: Why the recent over-emphasis
	on embodiment has held up progress}},
  BOOKTITLE = {{Creating Brain-like Intelligence}},
  PUBLISHER = {Springer-Verlag},
  YEAR = {2009},
  EDITOR = {B. Sendhoff and E. Koerner and O. Sporns and H. Ritter and K. Doya},
  PAGES = {248--277},
  ADDRESS = {Berlin},
  NOTE = {http://www.cs.bham.ac.uk/research/projects/cosy/papers/\#tr0804},
  ABSTRACT = {Some issues concerning requirements for architectures, mechanisms,
	ontologies and forms of representation in intelligent human-like
	or animal-like robots are discussed. The tautology that a robot that
	acts and perceives in the world must be embodied is often combined
	with false premises, such as the premiss that a particular type of
	body is a requirement for intelligence, or for human intelligence,
	or the premiss that all cognition is concerned with sensorimotor
	interactions, or the premiss that all cognition is implemented in
	dynamical systems closely coupled with sensors and effectors. It
	is time to step back and ask what robotic research in the past decade
	has been ignoring. I shall try to identify some ma jor research gaps
	by a combination of assembling requirements that have been largely
	ignored and design ideas that have not been investigated -- partly
	because at present it is too difficult to make significant progress
	on those problems with physical robots, as too many different problems
	need to be solved simultaneously. In particular, the importance of
	studying some abstract features of the environment about which the
	animal or robot has to learn (extending ideas of J.J.Gibson) has
	not been widely appreciated.},
  DATE-ADDED = {2009-01-04 19:55:40 +0000},
  DATE-MODIFIED = {2009-01-06 09:03:50 +0000},
  KEYWORDS = {cosy; irlab},
  URL = {http://www.cognitivesystems.org/publications/sloman-honda.pdf}
}

@INCOLLECTION{Sloman:2008a,
  AUTHOR = {A. Sloman},
  TITLE = {{Putting the Pieces Together Again}},
  BOOKTITLE = {{Cambridge Handbook on Computational Psychology}},
  PUBLISHER = {Cambridge University Press},
  YEAR = {2008},
  EDITOR = {Ron Sun},
  CHAPTER = {26},
  PAGES = {684--709},
  ADDRESS = {New York},
  NOTE = {http://www.cs.bham.ac.uk/research/projects/cogaff/07.html\#710},
  ABSTRACT = {This is a 'preprint' for the final chapter of a Handbook of Computational
	Psychology which is currently in press. The differences between this
	and the version to be published include British vs American spelling
	and punctuation. This version also has a few footnotes that had to
	be excluded. For some reason the publisher did not want abstracts
	for each chapter, so there is no official abstract. The preprint
	version also includes a table of contents for the chapter (copied
	below).
	
	 Overview Instead of surveying achievements of AI and computational
	Cognitive Science as might be expected, this chapter complements
	the Editor's review of requirements for work on integrated systems
	in Chapter 1, by presenting a personal view of some of the major
	unsolved problems, and obstacles to solving them. It attempts to
	identify some major gaps, and to explain why progress has been much
	slower than many people expected. It also includes some recommendations
	for improving progress and for countering the fragmentation and factionalism
	of the research community.
	
	 It it is relatively easy to identify long term ambitions in vague
	terms, e.g. the aim of modelling human flexibility, human learning,
	human cognitive development, human language understanding or human
	creativity; but taking steps to fulfil the ambitions is fraught with
	difficulties. So progress in modelling human and animal cognition
	is slow despite many impressive narrow-focus achievements, including
	those reported in earlier chapters.
	
	 An attempt is made to explain why progress in producing realistic
	models of human and animal competences is slow, namely (a) the great
	difficulty of the problems, (b) failure to understand the breadth,
	depth and diversity of the problems, (c) the fragmentation of the
	research community and (d) social and institutional pressures against
	risky multi-disciplinary, long-term research. Advances in computing
	power, theory and techniques will not suffice to overcome these difficulties.
	Partial remedies are offered, namely identifying some of the unrecognised
	problems and suggesting how to plan research on the basis of `backward-chaining'
	from long term goals, in ways that may, perhaps, help warring factions
	to collaborate and provide new ways to select targets and assess
	progress. },
  DATE-ADDED = {2009-01-04 19:55:40 +0000},
  DATE-MODIFIED = {2009-01-06 09:03:50 +0000},
  KEYWORDS = {cosy; irlab},
  URL = {http://www.cognitivesystems.org/publications/sloman-sunbook.pdf}
}

@INCOLLECTION{Sloman:2009,
  AUTHOR = {Aaron Sloman},
  TITLE = {{Architectural and representational requirements for seeing processes
	and affordances}},
  BOOKTITLE = {{Computational Modelling in Behavioural Neuroscience: Closing the
	gap between neurophysiology and behaviour.}},
  PUBLISHER = {Psychology Press},
  YEAR = {2009},
  ADDRESS = {London},
  ABSTRACT = {This paper, combining the standpoints of philosophy and Artificial
	Intelligence with theoretical psychology, summarises several decades
	of investigation of the variety of functions of vision in humans
	and other animals, pointing out that biological evolution has solved
	many more problems than are normally noticed. Many of the phenomena
	discovered by psychologists and neuroscientists require sophisticated
	controlled laboratory settings and specialised measuring equipment,
	whereas the functions of vision reported here mostly require only
	careful attention to a wide range of everyday competences that easily
	go unnoticed. Currently available computer models and neural theories
	are very far from explaining those functions, so progress in explaining
	how vision works is more in need of new proposals for explanatory
	mechanisms than new laboratory data. Systematically formulating the
	requirements for such mechanisms is not easy. If we start by analysing
	familiar competences, that can suggest new experiments to clarify
	precise forms of these competences, how they develop within individuals,
	which other species have them, and how performance varies according
	to conditions. This will help to constrain requirements for models
	purporting to explain how the competences work. The paper ends with
	speculations regarding the need for new kinds of information-processing
	machinery to account for the phenomena. },
  ANNOTE = {http://www.cs.bham.ac.uk/research/projects/cosy/papers/\#tr0801},
  DATE-ADDED = {2009-01-04 19:55:40 +0000},
  DATE-MODIFIED = {2009-01-06 09:03:50 +0000},
  EDITORS = {Heinke, D. and Mavritsaki, E.},
  KEYWORDS = {cosy; irlab},
  URL = {http://www.cognitivesystems.org/publications/sloman-newmod.pdf}
}

@ARTICLE{Sloman:2008,
  AUTHOR = {A. Sloman},
  TITLE = {{The Well-Designed Young Mathematician}},
  JOURNAL = {Artificial Intelligence},
  YEAR = {2008},
  VOLUME = {172},
  PAGES = {2015--2034},
  NUMBER = {18},
  NOTE = {http://www.cs.bham.ac.uk/research/projects/cosy/papers/\#tr0807},
  ABSTRACT = {This paper complements McCarthy's ``The well designed child'', in
	part by putting it in a broader context, a space of sets of requirements
	and a space of designs, and in part by relating design features to
	development of mathematical competences. I moved into AI hoping to
	understand myself, especially hoping to understand how I could do
	mathematics. Over the ensuing four decades, my interactions with
	AI and other disciplines led to: design-based, cross-disciplinary
	investigations of requirements, especial those arising from interactions
	with a complex environment; a draft partial ontology for describing
	spaces of possible architectures, especially virtual machine architectures;
	investigations of how different forms of representation relate to
	different functions; analysis of biological nature/nurture tradeoffs
	and their relevance to machines; studies of control issues in a complex
	architecture; and showing how what can occur in such an architecture
	relates to our intuitive concepts of motivation, feeling, preferences,
	emotions, attitudes, values, moods, consciousness, etc. I conjecture
	that working models of human vision can lead to models of spatial
	reasoning that would help to support Kant's view of mathematics by
	showing that human mathematical abilities are a natural extension
	of abilities produced by biological evolution that are not yet properly
	understood, and have barely been noticed by psychologists and neuroscientists.
	Some requirements for such models, are described, including aspects
	of our ability to interact with complex 3-D structures and processes
	that extend Gibson's ideas concerning action affordances, to include
	proto-affordances, epistemic affordances and deliberative affordances.
	Some of what a child learns about structures and processes starts
	as empirical then, as a result of reflective processes, can be recognised
	as necessary (e.g., mathematical) truths. These processes normally
	develop unnoticed in young children, but provide the basis for much
	creativity in behaviour, as well as leading, in some, to development
	of an interest in mathematics. We still need to understand what sort
	of self-monitoring and self-extending architecture, and what forms
	of representation, are required to make this possible. This paper
	does not presuppose that all mathematical learners can do logic,
	though some fairly general form of reasoning seems to be required.},
  DATE-ADDED = {2009-01-04 19:55:40 +0000},
  DATE-MODIFIED = {2009-01-06 09:03:50 +0000},
  EE = {http://dx.doi.org/10.1016/j.artint.2008.09.004},
  KEYWORDS = {cosy; irlab},
  URL = {http://www.cognitivesystems.org/publications/sloman-aij-08.pdf}
}

@INPROCEEDINGS{Sridharan/etal:2008,
  AUTHOR = {M. Sridharan and R. Dearden and J. Wyatt},
  TITLE = {{E-HiPPo}: {E}xtensions to {H}ierarchical {POMDP}-based {V}isual
	{P}lanning on a {R}obot},
  BOOKTITLE = {The 27th {PlanSIG} Workshop},
  YEAR = {2008},
  MONTH = {December 11-12},
  ABSTRACT = {One major challenge to the widespread deployment of mobile robots
	is the ability to autonomously tailor the sensory processing to the
	task on hand. In our prior work \cite{mohan:icaps08}, we proposed
	an approach for such general-purpose processing of visual input in
	an application domain where a robot and a human jointly converse
	about and manipulate objects on a tabletop by processing the regions
	of interest (ROIs) in input images. We posed the visual processing
	management problem as a partially observable Markov decision problem
	(POMDP), and introduced a hierarchical decomposition to make it tractable
	to plan with POMDPs. In this paper we analyze and eliminate some
	of the limitations of the existing approach. First, in addition to
	tackling visual actions that analyze the state of the world represented
	by the image, we show how to incorporate actions that can change
	the state. Secondly, we show how policy caching can be used to speed
	the planning performance and analyse the tradeoff between planning
	speed and plan quality.},
  ANNOTE = {Extensions to the ICAPS-08 paper...},
  BIB2HTML_FUNDING = {CoSy, CogX, Leverhulme},
  BIB2HTML_PUBTYPE = {Refereed Workshop},
  BIB2HTML_RESCAT = {Vision, Planning, Robotics},
  BIBAUTHOR = {smohan},
  DATE-ADDED = {2009-01-04 19:57:38 +0000},
  DATE-MODIFIED = {2009-01-06 09:03:50 +0000},
  KEYWORDS = {cosy; planning; vision; irlab},
  URL = {http://www.cognitivesystems.org/publications/sridharanetal08ehippo.pdf}
}

@INPROCEEDINGS{Sridharan/etal:2008a,
  AUTHOR = {M. Sridharan and J. Wyatt and R. Dearden},
  TITLE = {{HiPPo}: {H}ierarchical {POMDP}s for {P}lanning {I}nformation {P}rocessing
	and {S}ensing {A}ctions on a {R}obot},
  BOOKTITLE = {International Conference on Automated Planning and Scheduling (ICAPS)},
  YEAR = {2008},
  MONTH = {September 14-18},
  ABSTRACT = {Flexible general purpose robots need to tailor their visual processing
	to their task, on the fly. We propose a new approach to this within
	a planning framework, where the goal is to plan a sequence of visual
	operators to apply to the regions of interest (ROIs) in a scene.
	We pose the visual processing problem as a Partially Observable Markov
	Decision Process (POMDP). This requires probabilistic models of operator
	effects to quantitatively capture the unreliability of the processing
	actions, and thus reason precisely about trade-offs between plan
	execution time and plan reliability. Since planning in practical
	sized POMDPs is intractable we show how to ameliorate this intractability
	somewhat for our domain by defining a hierarchical POMDP. We compare
	the hierarchical POMDP approach with a Continual Planning (CP) approach.
	On a real robot visual domain, we show empirically that all the planning
	methods outperform naive application of all visual operators. The
	key result is that the POMDP methods produce more robust plans than
	either naive visual processing or the CP approach. In summary, we
	believe that visual processing problems represent a challenging and
	worthwhile domain for planning techniques, and that our hierarchical
	POMDP based approach to them opens up a promising new line of research.},
  ANNOTE = {Well, my first ICAPS paper on POMDP-based planning...},
  BIB2HTML_FUNDING = {CoSy, CogX, Leverhulme},
  BIB2HTML_PUBTYPE = {Refereed Conference},
  BIB2HTML_RESCAT = {Vision, Planning, Robotics},
  BIBAUTHOR = {smohan},
  DATE-ADDED = {2009-01-04 19:57:34 +0000},
  DATE-MODIFIED = {2009-01-06 09:03:50 +0000},
  KEYWORDS = {cosy; planning; vision; irlab},
  URL = {http://www.cognitivesystems.org/publications/sridharanetal08hippo.pdf}
}

@ARTICLE{Stachniss2008,
  AUTHOR = {C. Stachniss and O. Martinez Mozos and W. Burgard},
  TITLE = {Efficient Exploration of Unknown Indoor Environments using a Team
	of Mobile Robots},
  JOURNAL = {Annals of Mathematics and Artificial Intelligence},
  YEAR = {2008},
  VOLUME = {accepted}
}

@INPROCEEDINGS{stark08icvs,
  AUTHOR = {Michael Stark and Philipp Lies and Michael Zillich and Jeremy Wyatt
	and Bernt Schiele},
  TITLE = {Functional Object Class Detection Based on Learned Affordance Cues},
  BOOKTITLE = {6th International Conference on Computer Vision Systems (ICVS)},
  YEAR = {2008},
  MONTH = MAY,
  NOTE = {Accepted},
  ABSTRACT = {Current approaches to visual object class detection mainly focus on
	the recognition of abstract object categories, such as cars, motorbikes,
	mugs and bottles. Although these approaches have demonstrated impressive
	performance in terms of recognition, their restriction to abstract
	categories seems artificial and inadequate in the context of embodied,
	cognitive agents. Here, distinguishing objects according to functional
	aspects based on object affordances is vital for a meaningful human-machine
	interaction. In this paper, we propose a complete system for the
	detection of functional object classes, based on a representation
	of visually distinct hints on object affordances (affordance cues).
	It spans the complete cycle from tutor-driven acquisition of affordance
	cues, one-shot learning of corresponding object models, and detecting
	novel instances of functional object classes in real images.},
  LOCATION = {Santorini, Greece},
  URL = {http://www.cognitivesystems.org/publications/stark08icvs.pdf}
}

@INPROCEEDINGS{sturm08icra,
  AUTHOR = {Sturm, J. and Plagemann, C. and Burgard, W.},
  TITLE = {Unsupervised Body Scheme Learning through Self-Perception},
  BOOKTITLE = {Proc.~of the IEEE Int.~Conf.~on Robotics \& Automation (ICRA)},
  YEAR = {2008},
  PAGES = {3328--3333},
  ADDRESS = {Pasadena, CA, USA},
  ABSTRACT = {In this paper, we present an approach allowing a robot to learn a
	generative model of its own physical body from scratch using self-perception
	with a single monocular camera. Our approach yields a compact Bayesian
	network for the robot's kinematic structure including the forward
	and inverse models relating action commands and body pose. We propose
	to simultaneously learn local action models for all pairs of perceivable
	body parts from data generated through random ``motor babbling.''
	From this repertoire of local models, we construct a Bayesian network
	for the full system using the pose prediction accuracy on a separate
	cross validation data set as the criterion for model selection. The
	resulting model can be used to predict the body pose when no perception
	is available and allows for gradient-based posture control. In experiments
	with real and simulated manipulator arms, we show that our system
	is able to quickly learn compact and accurate models and to robustly
	deal with noisy observations.},
  URL = {http://www.cognitivesystems.org/publications/sturm08icra.pdf}
}

@INPROCEEDINGS{sturm08rss,
  AUTHOR = {Sturm, J. and Plagemann, C. and Burgard, W.},
  TITLE = {Adaptive Body Scheme Models for Robust Robotic Manipulation},
  BOOKTITLE = {Robotics: Science and Systems (RSS)},
  YEAR = {2008},
  ADDRESS = {Zurich, Switzerland},
  MONTH = {June},
  URL = {http://www.cognitivesystems.org/publications/sturm08rss.pdf}
}

@INPROCEEDINGS{sturm08rss-workshop,
  AUTHOR = {Sturm, J. and Plagemann, C. and Burgard, W.},
  TITLE = {Body Scheme Learning and Life-Long Adaptation for Robotic Manipulation},
  BOOKTITLE = {Proceedings of the Workshop on Robot Manipulation at Robotics: Science
	and Systems Conference (RSS)},
  YEAR = {2008},
  ADDRESS = {Zurich, Switzerland},
  MONTH = {June},
  URL = {http://www.cognitivesystems.org/publications/sturm08rss-workshop.pdf}
}

@BOOK{Thomaz/etal:2008-RSS,
  TITLE = {Interactive Robot Learning - RSS 2008 workshop},
  YEAR = {2008},
  AUTHOR = {A. Lockerd Thomaz and G.J.M. Kruijff and H. Jacobssonn and D. Skocaj},
  ADDRESS = {Zurich, Switzerland},
  MONTH = {June},
  ABSTRACT = {This workshop on Interactive Robot Learning will span the breadth
	of research questions at the intersection of Machine Learning and
	Human-Robot Interaction. Many future applications for autonomous
	robots bring them into human environments as helpful assistants to
	untrained users in homes, offices, hospitals, and more. These applica-
	tions will often require robots to flexibly adapt to the dynamic
	needs of human users. Rather than being pre-programmed at the factory
	with a fixed repertoire of skills, these personal robots will need
	to be able to quickly learn how to perform new tasks and skills from
	natural human instruction. Moreover, it is our belief that people
	should not have to learn a new form of interaction in order to teach
	these machines, that the robots should be able to take advantage
	of communication channels that are natural and intuitive for the
	human partner.},
  URL = {http://www.cognitivesystems.org/publications/InteractiveRobotLearning2008.pdf}
}

@INBOOK{Triebel2008,
  CHAPTER = {Studies in Classification, Data Analysis, and Knowledge Organization},
  PAGES = {293-300},
  TITLE = {Relational Learning in Mobile Robotics: An Application to Semantic
	Labeling of Objects in 2D and 3D Environment Maps},
  PUBLISHER = {Springer-Verlag},
  YEAR = {2008},
  AUTHOR = {R. Triebel and O. Mozos and W. Burgard}
}

@INPROCEEDINGS{ullah08icra,
  AUTHOR = {Ullah, M. M. and Pronobis, A. and Caputo, B. and Luo, J. and Jensfelt,
	P. and Christensen, H. I.},
  TITLE = {Towards Robust Place Recognition for Robot Localization},
  BOOKTITLE = {Proceedings of the IEEE International Conference on Robotics and
	Automation (ICRA'08)},
  YEAR = {2008},
  ADDRESS = {Pasadena, CA, USA},
  MONTH = {May},
  ABSTRACT = {Localization and context interpretation are two key competences for
	mobile robot systems. Visual place recognition, as opposed to purely
	geometrical models, holds promise of higher flexibility and association
	of semantics to the model. Ideally, a place recognition algorithm
	should be robust to dynamic changes and it should perform consistently
	when recognizing a room (for instance a corridor) in different geographical
	locations. Also, it should be able to categorize places, a crucial
	capability for transfer of knowledge and continuous learning. In
	order to test the suitability of visual recognition algorithms for
	these tasks, this paper presents a new database, acquired in three
	different labs across Europe. It contains image sequences of several
	rooms under dynamic changes, acquired at the same time with a perspective
	and omnidirectional camera, mounted on a socket. We assess this new
	database with an appearance based algorithm that combines local features
	with support vector machines through an ad-hoc kernel. Results show
	the effectiveness of the approach and the value of the database.},
  URL = {http://www.cognitivesystems.org/publications/ullah08icra.pdf}
}

@INPROCEEDINGS{wojekDAGM08b,
  AUTHOR = {Christian Wojek and Gyuri Dork{\'o} and Andre Schulz and Bernt Schiele},
  TITLE = {Sliding-Windows for Rapid Object-Class Localization: a Parallel Technique},
  BOOKTITLE = {Proceedings of DAGM},
  YEAR = {2008},
  MONTH = JUN,
  ABSTRACT = {This paper presents a fast ob ject-class localization frame- work
	implemented on a data parallel architecture currently available in
	many recent computers. Our case-study, the implementation of His-
	tograms of Oriented Gradients (HOG) descriptors, shows that just
	by using this recent programming model we can easily speed up an
	original CPU-only implementation by a factor of 24, making it unnecessary
	to use early rejection cascades that sacrifice classification performance,
	even in real-time conditions. Using recent techniques to program
	the Graphics Processing Unit (GPU) allows our method to scale-up
	to the latest, as well as to future improvements of the hardware,
	and have an expected additional speed-up from 2 to 4 on recent solutions.},
  URL = {http://www.cognitivesystems.org/publications/hoggpu.pdf}
}

@INPROCEEDINGS{wojekDAGM08a,
  AUTHOR = {Christian Wojek and Bernt Schiele},
  TITLE = {A performance evaluation of single and multi-cue people detection},
  BOOKTITLE = {Proceedings of DAGM},
  YEAR = {2008},
  MONTH = JUN,
  ABSTRACT = {Over the years a number of powerful people detectors have been proposed.
	While it is standard to test complete detectors on publicly available
	datasets, it is often unclear how the different components (e.g.
	features and classifiers) of the respective detectors compare. Therefore,
	this paper contributes a systematic comparison of the most prominent
	and successful people detectors. Based on this evaluation we also
	propose a new detector that outperforms the state-of-art on the INRIA
	person dataset by combining multiple cues.},
  URL = {http://www.cognitivesystems.org/publications/detector.pdf}
}

@INPROCEEDINGS{Wyatt/etal:2008,
  AUTHOR = {Jeremy Wyatt and Nick Hawes},
  TITLE = {Multiple Workspaces as an Architecture for Cognition},
  BOOKTITLE = {Proceedings of AAAI 2008 Fall Symposium on Biologically Inspired
	Cognitive Architectures},
  YEAR = {2008},
  NOTE = {To appear},
  ABSTRACT = {In this paper we describe insights for theories of natural intelligence
	that arise from recent advances in architectures for robot intelligence.
	In particular we advocate a sketch theory for the study of both natural
	and artificial intelligence that consists of a set of constraints
	on architectures. The sketch includes the use of multiple shared
	workspaces, parallel asynchronous refinement of shared representations,
	statistical integration of evidence within and across modalities,
	massively parallel prediction and content addressable memory to allow
	binding across workspaces.},
  DATE-ADDED = {2009-01-04 20:31:55 +0000},
  DATE-MODIFIED = {2009-01-06 09:03:50 +0000},
  KEYWORDS = {cosy; irlab},
  URL = {http://www.cognitivesystems.org/publications/wyatthawes08bica.pdf}
}

@ARTICLE{zender/etal:2008-ras_fs2hsc,
  AUTHOR = {H. Zender and O. Mart\'{\i}nez Mozos and P. Jensfelt and G.J.M. Kruijff
	and W. Burgard},
  TITLE = {Conceptual Spatial Representations for Indoor Mobile Robots},
  JOURNAL = {Robotics and Autonomous Systems},
  YEAR = {2008},
  VOLUME = {56},
  NUMBER = {6},
  MONTH = {June},
  NOTE = {Special Issue "From Sensors to Human Spatial Concepts"},
  ABSTRACT = {We present an approach for creating conceptual representations of
	human-made indoor environments using mobile robots. The concepts
	refer to spatial and functional properties of typical indoor environments.
	Following findings in cognitive psychology, our model is composed
	of layers representing maps at different levels of abstraction. The
	complete system is integrated in a mobile robot endowed with laser
	and vision sensors for place and ob ject recognition. The system
	also incorporates a linguistic framework that actively supports the
	map acquisition process, and which is used for situated dialogue.
	Finally, we discuss the capabilities of the integrated system.},
  URL = {http://www.cognitivesystems.org/publications/zender_etal08-ras-aam.pdf}
}



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