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cosyBib2007.bib
@INPROCEEDINGS{arras2007icra,
AUTHOR = {Arras, K. O. and Mart\'{i}nez Mozos, O. and Burgard, W.},
TITLE = {Using Boosted Features for the Detection of People in 2D Range Data},
BOOKTITLE = {Proc.~of the IEEE Int.~Conf.~on Robotics \& Automation (ICRA)},
YEAR = {2007},
ABSTRACT = {This paper addresses the problem of detecting people in two dimensional
range scans. Previous approaches have mostly used pre-defined features
for the detection and tracking of people. We propose an approach
that utilizes a supervised learning technique to create a classifier
that facilitates the detection of people. In particular, our approach
applies AdaBoost to train a strong classifier from simple features
of groups of neighboring beams corresponding to legs in range data.
Experimental results carried out with laser range data illustrate
the robustness of our approach even in cluttered office environments.},
URL = {http://www.cognitivesystems.org/publications/arras2007icra.pdf}
}
@INPROCEEDINGS{ballesta2007robomat,
AUTHOR = {Ballesta, M. and Gil, A. and Mart\'{i}nez Mozos, O. and Reinoso,
O.},
TITLE = {Local Descriptors for Visual SLAM},
BOOKTITLE = {Proc.~of the Workshop on Robotics and Mathematics},
YEAR = {2007},
ADDRESS = {Coimbra, Portugal},
ABSTRACT = {We present a comparison of several local image descriptors in the
context of visual Simultaneous Localization and Mapping (SLAM). In
visual SLAM a set of points in the environment are extracted from
images and used as landmarks. The points are represented by local
descriptors used to resolve the association between landmarks. In
this paper, we study the class separability of several descriptors
under changes in viewpoint and scale. Several experiments were carried
out using sequences of images in 2D and 3D scenes.},
URL = {http://www.cognitivesystems.org/publications/ballesta2007robomat.pdf}
}
@INPROCEEDINGS{Brenner/etal:2007,
AUTHOR = {Brenner, M. and Hawes, N. and Kelleher, J. and Wyatt, J.},
TITLE = {Mediating Between Qualitative and Quantitative Representations for
Task-Orientated Human-Robot Interaction},
BOOKTITLE = {Proc.~of the Twentieth International Joint Conference on Artificial
Intelligence (IJCAI)},
YEAR = {2007},
ADDRESS = {Hyderabad, India},
MONTH = {January},
ABSTRACT = {In human-robot interaction (HRI) it is essential that the robot interprets
and reacts to a human's utterances in a manner that reflects their
intended meaning. In this paper we present a collection of novel
techniques that allow a robot to interpret and execute spoken commands
describing manipulation goals involving qualitative spatial constraints
(e.g. ``put the red ball near the blue cube''). The resulting implemented
system integrates computer vision, potential field models of spatial
relationships, and action planning to mediate between the continuous
real world, and discrete, qualitative representations used for symbolic
reasoning.},
URL = {http://www.cognitivesystems.org/publications/brenneretal07ijcai.pdf}
}
@ARTICLE{Chappell/etal:2007,
AUTHOR = {Jackie Chappell and Aaron Sloman},
TITLE = {{Natural and artificial meta-configured altricial information-processing
systems}},
JOURNAL = {International Journal of Unconventional Computing},
YEAR = {2007},
VOLUME = {3},
PAGES = {211--239},
NUMBER = {3},
NOTE = {http://www.cs.bham.ac.uk/research/projects/cosy/papers/\#tr0609},
ABSTRACT = {The full variety of powerful information-processing mechanisms 'discovered'
by evolution has not yet been re-discovered by scientists and engineers.
By attending closely to the diversity of biological phenomena, we
may gain new insights into (a) how evolution happens, (b) what sorts
of mechanisms, forms of representation, types of learning and development
and types of architectures have evolved, (c) how to explain ill-understood
aspects of human and animal intelligence, and (d) new useful mechanisms
for artificial systems. We analyse tradeoffs common to both biological
evolution and engineering design, and propose a kind of architecture
that grows itself, using, among other things, genetically determined
meta-competences that deploy powerful symbolic mechanisms to achieve
various kinds of discontinuous learning, often through play and exploration,
including development of an 'exosomatic' ontology, referring to things
in the environment --- in contrast with learning systems that discover
only sensorimotor contingencies or adaptive mechanisms that make
only minor modifications within a fixed architecture. },
DATE-ADDED = {2009-01-04 19:40:56 +0000},
DATE-MODIFIED = {2009-01-06 09:03:50 +0000},
KEYWORDS = {cosy; irlab},
URL = {http://www.cognitivesystems.org/publications/ijuc.pdf}
}
@INPROCEEDINGS{sfCVPR07,
AUTHOR = {S. Fidler and A. Leonardis},
TITLE = {Towards Scalable Representations of Object Categories: Learning a
Hierarchy of Parts},
BOOKTITLE = {IEEE Computer Society Conference on Computer Vision and Pattern Recognition},
YEAR = {2007},
ADDRESS = {Minnesota, USA},
MONTH = {June},
ABSTRACT = {This paper proposes a novel approach to constructing a hierarchical
representation of visual input that aims to enable recognition and
detection of a large number of object categories. Inspired by the
principles of efficient indexing (bottom-up), robust matching (top-down),
and ideas of compositionality, our approach learns a hierarchy of
spatially flexible compositions, i.e. parts, in an unsupervised,
statistics-driven manner. Starting with simple, frequent features,
we learn the statistically most significant compositions (parts composed
of parts), which consequently define the next layer. Parts are learned
sequentially, layer after layer, optimally adjusting to the visual
data. Lower layers are learned in a category-independent way to obtain
complex, yet sharable visual building blocks, which is a crucial
step towards a scalable representation. Higher layers of the hierarchy,
on the other hand, are constructed by using specific categories,
achieving a category representation with a small number of highly
generalizable parts that gained their structural flexibility through
composition within the hierarchy. Built in this way, new categories
can be efficiently and continuously added to the system by adding
a small number of parts only in the higher layers. The approach is
demonstrated on a large collection of images and a variety of object
categories. Detection results confirm the effectiveness and robustness
of the learned parts.},
URL = {http://www.cognitivesystems.org/publications/cvpr07fidler.pdf}
}
@ARTICLE{Folkesson07a,
AUTHOR = {John Folkesson and Patric Jensfelt and Henrik Christensen},
TITLE = {The M-space Feature Representation for SLAM},
JOURNAL = {IEEE Transactions on Robotics},
YEAR = {2007},
VOLUME = {23},
PAGES = {1024--1035},
NUMBER = {5},
MONTH = OCT,
ABSTRACT = {In this paper a new feature representation for Simultaneous Localization
and Mapping (SLAM) is discussed. The representation addresses feature
symmetries and constraints explicitly to make the basic model numerically
robust. In previous SLAM work, complete initialization of features
is typically performed prior to introduction of a new feature into
the map. This results in delayed use of new data. To allow early
use of sensory data the new feature representation addresses the
use of features that initially have been partially observed. This
is achieved by explicitly modelling the sub-space of a feature that
has been observed. In addition to accounting for the special properties
of each feature type, the commonalities can be exploited in the new
representation to create a feature framework that allows for interchanging
of SLAM algorithms, sensor and features. Experimental results are
presented using a low-cost web-cam, a laser range scanner and combinations
thereof. },
URL = {http://www.cognitivesystems.org/publications/mspace.pdf}
}
@INPROCEEDINGS{Fritz/etal:2007,
AUTHOR = {M. Fritz and G.J.M. Kruijff and B. Schiele},
TITLE = {Cross-Modal Learning Of Visual Categories Using Different Levels
of Supervision},
BOOKTITLE = {Proceedings of the International Conference on Computer Vision Systems
(ICVS 2007)},
YEAR = {2007},
ADDRESS = {Bielefeld, Germany},
MONTH = {March},
ABSTRACT = {Today’s object categorization methods use either supervised or unsupervised
training methods. While supervised methods tend to produce more accurate
results, unsupervised methods are highly attrac- tive due to their
potential to use far more and unlabeled training data. This paper
proposes a novel method that uses unsupervised training to obtain
visual groupings of ob jects and a cross-modal learning scheme to
overcome inherent limitations of purely unsupervised training. The
method uses a uni?ed and scale-invariant ob ject representation that
al- lows to handle labeled as well as unlabeled information in a
coherent way. One of the potential settings is to learn ob ject category
models from many unlabeled observations and a few dialogue interactions
that can be ambiguous or even erroneous. First experiments demonstrate
the ability of the system to learn meaningful generalizations across
ob jects already from a few dialogue interactions. },
URL = {http://www.cognitivesystems.org/publications/fritz+etal-icvs07.pdf}
}
@INPROCEEDINGS{Hawes/etal:2007c,
AUTHOR = {Nick Hawes and Aaron Sloman and Jeremy Wyatt},
TITLE = {Towards an Empirical Exploration of Design Space},
BOOKTITLE = {Evaluating Architectures for Intelligence: Papers from the 2007 AAAI
Workshop},
YEAR = {2007},
EDITOR = {Gal A. Kaminka and Catherina R. Burghart},
PAGES = {31 -- 35},
ADDRESS = {Vancouver, Canada},
MONTH = {July},
PUBLISHER = {AAAI Press},
ABSTRACT = {In this paper we propose an empirical method for the comparison of
architectures designed to produce similar behaviour from an intelligent
system. The approach is based on the exploration of \emph{design
space} using similar designs that all satisfy the same requirements
in \emph{niche space}. An example of a possible application of this
method is given using a robotic system that has been implemented
using a software toolkit that has been designed to support architectural
experimentation.},
ANNOTE = {Technical Report WS-07-04},
DATE-ADDED = {2009-01-05 11:34:59 +0000},
DATE-MODIFIED = {2009-01-06 09:03:50 +0000},
KEYWORDS = {cosy; irlab},
URL = {http://www.cognitivesystems.org/publications/hawesetal07eai.pdf}
}
@INPROCEEDINGS{Hawes/etal:2007a,
AUTHOR = {Nick Hawes and Aaron Sloman and Jeremy Wyatt and Michael Zillich
and Henrik Jacobsson and Geert-Jan Kruijff and Michael Brenner and
Gregor Berginc and Danijel Sko\v{c}aj},
TITLE = {Towards an Integrated Robot with Multiple Cognitive Functions},
BOOKTITLE = {Proceedings of the Twenty-Second AAAI Conference on Artificial Intelligence
(AAAI 2008)},
YEAR = {2007},
EDITOR = {Robert C. Holte and Adele Howe},
PAGES = {1548 -- 1553},
ADDRESS = {Vancouver, Canada},
MONTH = {July},
PUBLISHER = {AAAI Press},
ABSTRACT = {We present integration mechanisms for combining heterogeneous components
in a situated information processing system, illustrated by a cognitive
robot able to collaborate with a human and display some understanding
of its surroundings. These mechanisms include an architectural schema
that encourages parallel and incremental information processing,
and a method for binding information from distinct representations
that when faced with rapid change in the world can maintain a coherent,
though distributed, view of it. Provisional results are demonstrated
in a robot combining vision, manipulation, language, planning and
reasoning capabilities interacting with a human and manipulable objects.
},
DATE-ADDED = {2009-01-02 11:37:59 +0000},
DATE-MODIFIED = {2009-01-06 09:03:50 +0000},
KEYWORDS = {cosy, integration; irlab},
URL = {http://www.cognitivesystems.org/publications/hawesetal07playmate.pdf}
}
@INPROCEEDINGS{Hawes/etal:2007,
AUTHOR = {Nick Hawes and Michael Zillich and Jeremy Wyatt},
TITLE = {{BALT} \& {CAST}: Middleware for Cognitive Robotics},
BOOKTITLE = {Proceedings of IEEE RO-MAN 2007},
YEAR = {2007},
PAGES = {998 -- 1003},
MONTH = {August},
ABSTRACT = {In this paper we present a toolkit for implementing architectures
for intelligent robotic systems. This toolkit is based on an architecture
schema (a set of architecture design rules). The purpose of both
the schema and toolkit is to facilitate research into information-processing
architectures for state-of-the-art intelligent robots, whilst providing
engineering solutions for the development of such systems. A robotic
system implemented using the toolkit is presented to demonstrate
its key features.},
DATE-ADDED = {2009-01-02 13:27:34 +0000},
DATE-MODIFIED = {2009-01-06 09:03:50 +0000},
KEYWORDS = {cosy, cast, integration; irlab},
LOCATION = {Jeju, Korea},
URL = {http://www.cognitivesystems.org/publications/hawesetal07cast.pdf}
}
@INPROCEEDINGS{Jacobsson/etal:2007-ijcnn,
AUTHOR = {Jacobsson, H. and Frank, S.L. and Federici, D.},
TITLE = {Automated abstraction of dynamic neural systems for natural language
processing},
BOOKTITLE = {Proceedings of IJCNN 2007},
YEAR = {2007},
ABSTRACT = {This paper presents a variant of the Crystallizing Substochastic Sequential
Machine Extractor (CrySSMEx), an algorithm capable of extracting
finite state descriptions of dynamic systems such as recurrent neural
networks, without any regard to their topology or weights. The algorithm
is applied to a network performing a language prediction task. The
extracted state machines provide a very detailed view of the operations
of the RNN by abstracting and discretizing its functional behaviour.
Here we extend previous work also by extracting state machines in
Moore, rather than in Mealy, format. This subtle difference opens
up the rule extractor to more domains, including sensorimotor modelling
of autonomous robotic systems. Experiments are also conducted on
far more input symbols, providing a greater insight into the behaviour
of the algorithm. },
URL = {http://www.cognitivesystems.org/publications/ijcnn2007.pdf}
}
@INPROCEEDINGS{Jacobsson/etal:2007,
AUTHOR = {Henrik Jacobsson and Nick Hawes and Geert-Jan Kruijff and Jeremy
Wyatt},
TITLE = {Crossmodal Content Binding in Information-Processing Architectures},
BOOKTITLE = {Symposium on Language and Robots (LangRo 2007)},
YEAR = {2007},
EDITOR = {Luis Seabra Lopes and Tony Belpaeme and Stephen J. Cowley },
PAGES = {43--52},
ADDRESS = {Aveiro, Portugal},
MONTH = {December},
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 of its sensors on its own could provide. Second,
it needs to combine high-level representations (such as those for
planning and dialogue) with its 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 other approaches, can be combined
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.},
ANNOTE = {Deprecated! Please see the HRI paper of the same name instead. },
DATE-ADDED = {2009-01-05 11:32:14 +0000},
DATE-MODIFIED = {2009-01-06 09:03:50 +0000},
KEYWORDS = {cosy; irlab},
URL = {http://www.cognitivesystems.org/publications/jacobssonetal07binding.pdf}
}
@INPROCEEDINGS{jacobssonLANGRO07,
AUTHOR = {H. Jacobsson and N. Hawes and D. Sko\v{c}aj and GJ Kruijff},
TITLE = {Interactive learning and cross-modal binding - a combined approach},
BOOKTITLE = {Language and robots : proceedings of the symposium},
YEAR = {2007},
PAGES = {53-54},
ADDRESS = {Aveiro, Portugal},
MONTH = {December},
URL = {http://www.cognitivesystems.org/publications/jacobssonLANGRO07.pdf}
}
@INPROCEEDINGS{Jacobsson/etal:2007-icra,
AUTHOR = {H. Jacobsson and G.J.M. Kruijff and M. Staudte},
TITLE = {From Rule Extraction to Active Learning Symbol Grounding},
BOOKTITLE = {Proceedings of the ICRA-07 Workshop on Concept Learning for Embodied
Agents},
YEAR = {2007},
ADDRESS = {Rome, Italy},
MONTH = {April},
ABSTRACT = {The paper focuses on a fundamental learning problem in adaptive, embodied
cognitive systems: Namely, how to learn discrete models of situated,
embodied experience which can act as a mediation between sensori-motoric
experience and high-level cognitive processes. The paper suggests
to address the problem using a combination of bottom up active learning
of embodied concepts solely on the basis of the actions and perceptions
of the robot, and top-down information obtained through interaction
with other agents. The embodied concepts are constructed to be informative
for the robot in terms of its sensorimotor prediction capability.
From that point the effort of constructing humanlike concepts is
shifted towards producing a translation between the sensorimotor
based bottom-up on- tology and more conventional top-down constructed
ontologies. The suggested framework is based on a parameter free
rule extraction algorithm that successfully has been applied to the
problem of creating ?nite state descriptions of large, complex and
even chaotic simulated dynamic systems. We will brie?y describe how
this algorithm can be ported to an autonomous robot domain. },
URL = {http://www.cognitivesystems.org/publications/jacobsson+etal.icra07.pdf}
}
@INPROCEEDINGS{Jacobsson/etal:2007-pascal,
AUTHOR = {Jacobsson, H. and Kruijff, G.J.M. and Staudte, M.},
TITLE = {Language Acquisition from Neural and Sensorimotor Systems},
BOOKTITLE = {Proceedings of the PASCAL workshop on Machine Learning and Cognitive
Science of Language Acquisition},
YEAR = {2007},
ABSTRACT = {A fundamental learning problem in adaptive, embodied cognitive systems
is how to learn discrete models of situated experience which can
mediate between sensorimotoric expe- rience and high-level cognitive
processes (such as language and planning)the dynamic system is mapped
to the extracted discrete states (i.e. grounding the grammar in the
system). The paper discusses how this hierarchical description can
be considered CrySSMEx’s ontology of the state space. },
URL = {http://www.cognitivesystems.org/publications/mlcsla_abstract_2007.pdf}
}
@ARTICLE{kersting07ar,
AUTHOR = {Kersting, K. and Plagemann, C. and Cocora, A. and Burgard, W. and
De Raedt, L.},
TITLE = {Learning to Transfer Optimal Navigation Policies},
JOURNAL = {Advanced Robotics. Special Issue on Imitative Robots},
YEAR = {2007},
VOLUME = {21},
NUMBER = {9},
MONTH = {September},
ABSTRACT = {Autonomous agents that act in the real world utilizing sensory input
greatly rely on the ability to plan their actions and to transfer
these skills across tasks. The majority of path planning approaches
for mobile robots, however, solve the current navigation problem
from scratch given the current and goal configuration of the robot.
Consequently, these approaches yield highly efficient plans for the
specific situation, but the computed policies typically do not transfer
to other, similar tasks. In this paper, we propose to apply techniques
from statistical relational learning to the path planning problem.
More precisely, we propose to learn relational decision trees as
abstract navigation strategies from example paths. Relational abstraction
has several interesting and important properties. First, it allows
a mobile robot to imitate navigation behavior shown by users or by
optimal policies. Second, it yields comprehensible models of behavior.
Finally, a navigation policy learned in one environment naturally
transfers to unknown environments. In several experiments with real
robots and in simulated runs, we demonstrate that our approach yields
efficient navigation plans. We show that our system is robust against
observation noise and can outperform hand-crafted policies.},
URL = {http://www.cognitivesystems.org/publications/kersting07ar.pdf}
}
@INPROCEEDINGS{kersting07icml,
AUTHOR = {Kersting, K. and Plagemann, C. and Pfaff, P. and Burgard, W.},
TITLE = {Most Likely Heteroscedastic Gaussian Process Regression},
BOOKTITLE = {International Conference on Machine Learning (ICML)},
YEAR = {2007},
ADDRESS = {Corvallis, Oregon, USA},
MONTH = {March},
ABSTRACT = {This paper presents a novel Gaussian process (GP) approach to regression
with input-dependent noise rates. We follow Goldberg et al.'s approach
and model the noise variance using a second GP in addition to the
GP governing the noise-free output value. In contrast to Goldberg
et al., however, we do not use a Markov chain Monte Carlo method
to approximate the posterior noise variance but a most likely noise
approach. The resulting model is easy to implement and can directly
be used in combination with various existing extensions of the standard
GPs such as sparse approximations. Extensive experiments on both
synthetic and real-world data, including a challenging perception
problem in robotics, show the effectiveness of most likely heteroscedastic
GP regression.},
URL = {http://www.cognitivesystems.org/publications/kersting07icml.pdf}
}
@INPROCEEDINGS{Kruijff/Brenner:2007,
AUTHOR = {G.J.M. Kruijff and M. Brenner},
TITLE = {Modelling Spatio-Temporal Comprehension in Situated Human-Robot Dialogue
as Reasoning about Intentions and Plans},
BOOKTITLE = {Proceedings of the Symposium on Intentions in Intelligent Systems},
YEAR = {2007},
ADDRESS = {Stanford University, Palo Alto, CA, USA},
MONTH = {March},
PUBLISHER = {AAAI Spring Symposium Series 2007}
}
@INPROCEEDINGS{Kruijff/Staudte:2007,
AUTHOR = {G.J.M. Kruijff and M. Staudte},
TITLE = {Producing believeable robot gaze when comprehending visually situated
dialogue},
BOOKTITLE = {Language and Robots: Proceedings from the Symposium (LangRo'2007)},
YEAR = {2007},
ADDRESS = {Aveiro, Portugal},
MONTH = {December},
ABSTRACT = {The paper presents an implemented approach to producing robot gaze
during comprehending visually situated dialogue. The approach is
based on an incremental model for processing situated dialogue. In
this model, utterance interpretations are build step-by-step, in
a "left-to-right" fashion. At each step, grammatical and dialogue-level
information is combined with information about the visually situated
context. As a consequence, utterance processing can be guided so
as to construct only situationally appropriate interpretations. Furthermore,
at each step a set of visual referents is determined, to which the
unfolding utterance meaning is currently making reference. In the
approach, this information is used to drive robot gaze, letting the
robot change its fixation onto the most recent visual referent. The
underlying assumption is that gaze behavior helps to establish joint
attention ("common ground") in a dialogue, if there is congruency
between where the robot is looking, and what the (intended) visual
referent is. The paper reports on a pilot study in which this assumption
is studied. The results show statistically significant interactions
between congruence, believability, and appropriateness of referring
expression. },
URL = {http://www.cognitivesystems.org/publications/main.gaze.langro2007.pdf}
}
@ARTICLE{Kruijff/etal:2007-JARS,
AUTHOR = {G.J.M. Kruijff and H. Zender and P. Jensfelt and H.I. Christensen},
TITLE = {Situated Dialogue and Spatial Organization: What, Where... and Why?},
JOURNAL = {International Journal of Advanced Robotic Systems},
YEAR = {2007},
VOLUME = {4},
NUMBER = {2},
NOTE = {Special Issue on Human and Robot Interactive Communication},
ABSTRACT = {The paper presents an HRI architecture for human-augmented mapping,
which has been implemented and tested on an autonomous mobile robotic
platform. Through interaction with a human, the robot can augment
its autonomously acquired metric map with qualitative information
about locations and objects in the environment. The system implements
various interaction strategies observed in independently performed
Wizard-of-Oz studies. The paper discusses an ontology-based approach
to multi-layered conceptual spatial mapping that provides a common
ground for human-robot dialogue. This is achieved by combining acquired
knowledge with innate conceptual commonsense knowledge in order to
infer new knowledge. The architecture bridges the gap between the
rich semantic representations of the meaning expressed by verbal
utterances on the one hand and the robot's internal sensor-based
world representation on the other. It is thus possible to establish
references to spatial areas in a situated dialogue between a human
and a robot about their environment. The resulting conceptual descriptions
represent qualitative knowledge about locations in the environment
that can serve as a basis for achieving a notion of situational awareness.},
URL = {http://www.cognitivesystems.org/publications/kruijff_etal07-jars.pdf}
}
@INPROCEEDINGS{Kruijff/etal:2007-sitdial,
AUTHOR = {Geert-Jan M. Kruijff and Pierre Lison and Trevor Benjamin and Henrik
Jacobsson and Nick Hawes},
TITLE = {Incremental, multi-level processing for comprehending situated dialogue
in human-robot interaction},
BOOKTITLE = {Symposium on Language and Robots (LangRo 2007)},
YEAR = {2007},
EDITOR = {Luis Seabra Lopes and Tony Belpaeme and Stephen J. Cowley},
ADDRESS = {Aveiro, Portugal},
MONTH = {December},
ABSTRACT = {The paper presents work in progress on an implemented model of situated
dialogue processing. The underlying assumption is that to understand
situated dialogue, communicated meaning needs to be related to the
situation(s) it refers to. The model couples incremental processing
to a notion of bidirectional connectivity, inspired by how humans
process visually situated language. Analyzing an utterance in a ''word-by-word,
left-to-right'' fashion, a representation of possible utterance interpretations
is gradually built up. In a top-down fashion, the model tries to
ground these interpretations in situation awareness, through which
they can prime what is focused on in a situation. In a bottom-up
fashion, the (im)possibility to ground certain interpretations primes
how the analysis of the utterance further unfolds. The paper discusses
the implementation of the model in a distributed, cognitive architecture
for human-robot interaction, and presents an evaluation on a test
suite. The evaluation quantifies the effects linguistic interpretation
has on priming utterance processing, and discusses how the evaluation
can be extended to include situation context.},
DATE-ADDED = {2009-01-05 11:46:50 +0000},
DATE-MODIFIED = {2009-01-06 09:03:50 +0000},
KEYWORDS = {cosy; irlab},
URL = {http://www.cognitivesystems.org/publications/kruijffetal07incremental.pdf}
}
@INPROCEEDINGS{lang07rss,
AUTHOR = {Lang, T. and Plagemann, C. and Burgard, W.},
TITLE = {Adaptive Non-Stationary Kernel Regression for Terrain Modeling},
BOOKTITLE = {Robotics: Science and Systems (RSS)},
YEAR = {2007},
ADDRESS = {Atlanta, Georgia, USA},
MONTH = {June},
ABSTRACT = {Three-dimensional digital terrain models are of fundamental importance
in many areas such as the geo-sciences and outdoor robotics. Accurate
modeling requires the ability to deal with a varying data density
and to balance smoothing against the preservation of discontinuities.
The latter is particularly important for robotics applications, as
discontinuities that arise, for example, at steps, stairs, or building
walls are important features for path planning or terrain segmentation
tasks. In this paper, we present an extension of the well-established
Gaussian process regression technique, that utilizes non-stationary
covariance functions to locally adapt to the structure of the terrain
data. In this way, we achieve strong smoothing in flat areas and
along edges and at the same time preserve edges and corners. The
derived model yields predictive height distributions for arbitrary
locations of the terrain and therefore allows us to fill gaps in
data and to perform conservative predictions in occluded areas.},
URL = {http://www.cognitivesystems.org/publications/lang07rss.pdf}
}
@INPROCEEDINGS{leonardisISRR07,
AUTHOR = {A. Leonardis and S. Fidler},
TITLE = {Learning hierarchical representations of object categories for robot
vision},
BOOKTITLE = {13th International Symposium of Robotics Research (ISRR)},
YEAR = {2007},
ADDRESS = {Hiroshima, Japan},
MONTH = {November},
ABSTRACT = {This paper presents our recently developed approach to constructing
a hierarchical representation of visual input that aims to enable
recognition and detection of a large number of object categories.
Inspired by the principles of efficient indexing, robust matching,
and ideas of compositionality, our approach learns a hierarchy of
spatially flexible compositions, i.e. parts, in an unsupervised,
statistics-driven manner. Starting with simple, frequent features,
we learn the statistically most significant compositions (parts composed
of parts), which consequently define the next layer. Parts are learned
sequentially, layer after layer, optimally adjusting to the visual
data. Lower layers are learned in a category-independent way to obtain
complex, yet sharable visual building blocks, which is a crucial
step towards a scalable representation. Higher layers of the hierarchy,
on the other hand, are constructed by using specific categories,
achieving a category representation with a small number of highly
generalizable parts that gained their structural flexibility through
composition within the hierarchy. Built in this way, new categories
can be efficiently and continuously added to the system by adding
a small number of parts only in the higher layers. The approach is
demonstrated on a large collection of images and a variety of object
categories.},
URL = {http://www.cognitivesystems.org/publications/isrr07LeonardisFidler.pdf}
}
@INPROCEEDINGS{Looije/etal:2007,
AUTHOR = {R. Looije and M. Neerincx and G.J.M. Kruijff},
TITLE = {Affective Collaborative Robots for Safety \& Crisis Management in
the Field},
BOOKTITLE = {Proceedings of the 4th International Conference on Information Systems
for Crisis Response and Management (ISCRAM 2007)},
YEAR = {2007},
ADDRESS = {Delft, The Netherlands},
MONTH = {May},
ABSTRACT = {The lack of human-robot collaboration currently presents a bottleneck
to widespread use of robots in urban search & rescue (USAR) missions.
The paper argues that an important aspect of realizing human-robot
collaboration will be collaborative control, and the recognition
and expression of affect. Affective collaborative robots can enhance
joint human-robot performance by adapting the robot’s (social) role
and interaction to the user’s affective state and the context . Current
USAR robots lack these capabilities. This paper presents theory,
application domains, and requirements for architectures to implement
these capabilities in robots. Based on methods from cognitive architectures,
affective computing, and human-robot interaction, three core functions
of affective collaborative robots can be realized: sliding autonomy,
affective communication, and adaptive attitude. These robot functions
can substantially enhance the efficiency and effectiveness of rescue
workers and meanwhile reduce their cognitive workload. Furthermore,
robots with such functions can approach civilians in the field appropriately.}
}
@INPROCEEDINGS{luo07iros,
AUTHOR = {Luo, J. and Pronobis, A. and Caputo, B. and Jensfelt, P.},
TITLE = {Incremental Learning for Place Recognition in Dynamic Environments},
BOOKTITLE = {Proceedings of the IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS'07)},
YEAR = {2007},
ADDRESS = {San Diego, CA, USA},
MONTH = {October},
ABSTRACT = {Vision-based place recognition is a desirable feature for an autonomous
mobile system. In order to work in realistic scenarios, visual recognition
algorithms should be adaptive, i.e. should be able to learn from
experience and adapt continuously to changes in the environment.
This paper presents a discriminative incremental learning approach
to place recognition. We use a recently introduced version of the
incremental SVM, which allows to control the memory requirements
as the system updates its internal representation. At the same time,
it preserves the recognition performance of the batch algorithm.
In order to assess the method, we acquired a database capturing the
intrinsic variability of places over time. Extensive experiments
show the power and the potential of the approach.},
URL = {http://www.cognitivesystems.org/publications/luo07iros.pdf}
}
@ARTICLE{MartinezMozos07b,
AUTHOR = {Mart\'{i}nez Mozos, O. and Triebel, R. and Jensfelt, P. and Rottmann,
A. and Burgard, W.},
TITLE = {Supervised semantic labeling of places using information extracted
from sensor data},
JOURNAL = {Robotics and Autonomous Systems},
YEAR = {2007},
VOLUME = {55},
PAGES = {391--402},
NUMBER = {5},
MONTH = {May},
ABSTRAC = {Indoor environments can typically be divided into places with different
functionalities like corridors, rooms or doorways. The ability to
learn such semantic categories from sensor data enables a mobile
robot to extend the representation of the environment facilitating
the interaction with humans. As an example, natural language terms
like ``corridor" or ``room" can be used to communicate the position
of the robot in a map in a more intuitive way. In this work, we first
propose an approach based on supervised learning to classify the
pose of a mobile robot into semantic classes. Our method uses AdaBoost
to boost simple features extracted from sensor range data into a
strong classifier. We present two main applications of this approach.
Firstly, we show how our approach can be utilized by a moving robot
for an online classification of the poses traversed along its path
using a hidden Markov model. In this case we additionally use as
features objects extracted from images. Secondly, we introduce an
approach to learn topological maps from geometric maps by applying
our semantic classification procedure in combination with a probabilistic
relaxation method. Alternatively, we apply associative Markov networks
to classify geometric maps and compare the results with the relaxation
approach. Experimental results obtained in simulation and with real
robots demonstrate the effectiveness of our approach in various indoor
environments.},
URL = {http://www.cognitivesystems.org/publications/mozos2007RAS.pdf}
}
@INPROCEEDINGS{Mozos/etal:2007,
AUTHOR = {O. Mart\'{\i}nez Mozos and P. Jensfelt and H. Zender and G.J.M. Kruijff
and W. Burgard},
TITLE = {An Integrated System for Conceptual Spatial Representations of Indoor
Environments for Mobile Robots},
BOOKTITLE = {Proceedings of the IROS 2007 Workshop: From Sensors to Human Spatial
Concepts (FS2HSC)},
YEAR = {2007},
ADDRESS = {San Diego, CA, USA},
MONTH = {November},
ABSTRACT = {We present an integrated approach for creating conceptual representations
of human-made environments using mobile robots. The concepts represent
spatial and functional properties of typical indoor environments.
Our model is composed of layers which represent maps at different
levels of abstraction. The complete system was integrated in a service
robot which is endowed with laser and vision sensors for place and
object recognition. It also incorporates a linguistic framework that
actively supports the map acquisition process and is used for situated
dialogue. In the experiments we show how the robot acquires the conceptual
information and how it is used for situational and functional awareness.
},
URL = {http://www.cognitivesystems.org/publications/mozos_etal07-irosws.pdf}
}
@INPROCEEDINGS{Mozos/etal:2007-icra,
AUTHOR = {O. Martinez Mozos and P. Jensfelt and H. Zender and G.J.M. Kruijff
and W. Burgard},
TITLE = {From Labels to Semantics: An Integrated System for Conceptual Spatial
Representations of Indoor Environments for Mobile Robots},
BOOKTITLE = {Proceedings of the ICRA-07 Workshop on Semantic Information in Robotics},
YEAR = {2007},
ADDRESS = {Rome, Italy},
MONTH = {April},
ABSTRACT = {We present an integrated approach for creating conceptual representations
of human-made environments using mobile robots. The concepts represent
spatial and functional properties of typical indoor environments.
Our model is composed of layers which represent maps at different
levels of abstraction. The complete system was integrated in a service
robot which is endowed with laser and vision sensors for place and
object recognition. It also incorporates a linguistic framework that
actively supports the map acquisition process and is used for situated
dialogue. In the experiments we show how the robot acquires the conceptual
information and how it is used for situational and functional awareness.
},
URL = {http://www.cognitivesystems.org/publications/mozos_etal07-icraws.pdf}
}
@ARTICLE{MartinezMozos07a,
AUTHOR = {Oscar Mart\'{i}nez Mozos and Rudolph Triebel and Patric Jensfelt
and Axel Rottmann and Wolfram Burgard},
TITLE = {Supervised Semantic Labeling of Places using Information Extracted
from Laser and Vision Sensor Data},
JOURNAL = {Robotics and Autonomous Systems Journal},
YEAR = {2007},
VOLUME = {55},
PAGES = {391--402},
NUMBER = {5},
MONTH = MAY,
ABSTRACT = {Indoor environments can typically be divided into places with different
functionalities like corridors, rooms or doorways. The ability to
learn such semantic categories from sensor data enables a mobile
robot to extend the representation of the environment facilitating
the interaction with humans. As an example, natural language terms
like ``corridor" or ``room" can be used to communicate the position
of the robot in a map in a more intuitive way. In this work, we first
propose an approach based on supervised learning to classify the
pose of a mobile robot into semantic classes. Our method uses AdaBoost
to boost simple features extracted from sensor range data into a
strong classifier. We present two main applications of this approach.
Firstly, we show how our approach can be utilized by a moving robot
for an online classification of the poses traversed along its path
using a hidden Markov model. In this case we additionally use as
features objects extracted from images. Secondly, we introduce an
approach to learn topological maps from geometric maps by applying
our semantic classification procedure in combination with a probabilistic
relaxation method. Alternatively, we apply associative Markov networks
to classify geometric maps and compare the results with the relaxation
approach. Experimental results obtained in simulation and with real
robots demonstrate the effectiveness of our approach in various indoor
environments. },
URL = {http://www.cognitivesystems.org/publications/mozos2007RAS.pdf}
}
@INPROCEEDINGS{doICCV07,
AUTHOR = {Du\v{s}an Omer\v{c}evi\v{c} and Ondrej Drbohlav and Ale\v{s} Leonardis},
TITLE = {High-dimensional feature matching: Employing the concept of meaningful
nearest neighbors},
BOOKTITLE = {Eleventh IEEE International Conference on Computer Vision ICCV 2007},
YEAR = {2007},
ADDRESS = {Rio de Janeiro, Brazil},
MONTH = {October 14-20},
ABSTRACT = {High-dimensional feature matching using nearest neighbors search is
an important problem in image matching using local invariant features.
In this work we highlight effects pertinent to high-dimensional spaces
that are significant for matching, yet have not been explicitly accounted
for in previous work. In our approach, we require any nearest neighbor
to be meaningful, that is, sufficiently close to a query feature
such that it is an outlier to a background feature distribution.
We estimate the background feature distribution from the extended
query feature neighborhood. Based on the concept of meaningful nearest
neighbors, we have developed a novel matching method and evaluated
its performance by conducting image matching on two challenging image
data sets. A superior performance is shown in comparison to several
state of the art approaches. To speed-up nearest neighbors search
in high-dimensions, we have developed a novel method for approximate
near neighbor search. This method provides a ten-fold speed-up over
an exhaustive search even for high dimensional spaces and retains
excellent approximation to an exact nearest neighbor search.},
URL = {http://www.cognitivesystems.org/publications/omercevicdrbohlavleonardis-iccv2007.pdf}
}
@INPROCEEDINGS{plagemann07ijcai,
AUTHOR = {Plagemann, C. and Fox, D. and Burgard, W.},
TITLE = {Efficient Failure Detection on Mobile Robots Using Particle Filters
with Gaussian Process Proposals},
BOOKTITLE = {Proc.~of the Twentieth International Joint Conference on Artificial
Intelligence (IJCAI)},
YEAR = {2007},
ADDRESS = {Hyderabad, India},
ABSTRACT = {The ability to detect failures and to analyze their causes is one
of the preconditions of truly autonomous mobile robots. Especially
online failure detection is a complex task, since the effects of
failures are typically difficult to model and often resemble the
noisy system behavior in a fault-free operational mode. In this paper
we present an approach that applies Gaussian process classification
and regression techniques for learning highly effective proposal
distributions of a particle filter that is applied to track the state
of the system. As a result, the efficiency and robustness of the
state estimation process is substantially improved. In practical
experiments carried out with a real robot we demonstrate that our
system is capable of detecting collisions with unseen obstacles while
at the same time estimating the changing point of contact with the
obstacle.},
URL = {http://www.cognitivesystems.org/publications/plagemann07ijcai.pdf}
}
@INPROCEEDINGS{plagemann07rss,
AUTHOR = {Plagemann, C. and Kersting, K. and Pfaff, P. and Burgard, W.},
TITLE = {Gaussian Beam Processes: A Nonparametric Bayesian Measurement Model
for Range Finders},
BOOKTITLE = {Robotics: Science and Systems (RSS)},
YEAR = {2007},
ADDRESS = {Atlanta, Georgia, USA},
MONTH = {June},
ABSTRACT = {In probabilistic mobile robotics, the development of measurement models
plays a crucial role as it directly influences the efficiency and
the robustness of the robot's performance in a great variety of tasks
including localization, tracking, and map building. In this paper,
we present a novel probabilistic measurement model for range finders,
called Gaussian Beam Processes, which treats the measurement modeling
task as a nonparametric Bayesian regression problem and solves it
using Gaussian processes. The major advantage of our approach lies
in the smoothness of the resulting model which appropriately represents
correlations between adjacent beams using covariance functions. Moreover,
the Gaussian process treatment results in a sound probabilistic measurement
model with a pool of well-established techniques for likelihood estimation
and range prediction for an arbitrary number of beams. Experiments
on real world and synthetic data show that Gaussian Beam Processes
combine the advantages of two popular measurement models.},
URL = {http://www.cognitivesystems.org/publications/plagemann07rss.pdf}
}
@INPROCEEDINGS{pronobis07iros,
AUTHOR = {Pronobis, A. and Caputo, B.},
TITLE = {Confidence-based Cue Integration for Visual Place Recognition},
BOOKTITLE = {Proceedings of the IEEE/RSJ International Conference on Intelligent
Robots and Systems (IROS'07)},
YEAR = {2007},
ADDRESS = {San Diego, CA, USA},
MONTH = {October},
ABSTRACT = {A distinctive feature of intelligent systems is their capability to
analyze their level of expertise for a given task; in other words,
they know what they know. As a way towards this ambitious goal, this
paper presents a recognition algorithm able to measure its own level
of confidence and, in case of uncertainty, to seek for extra information
so to increase its own knowledge and ultimately achieve better performance.
We focus on the visual place recognition problem for topological
localization, and we take an SVM approach. We propose a new method
for measuring the confidence level of the classification output,
based on the distance of a test image and the average distance of
training vectors. This method is combined with a discriminative accumulation
scheme for cue integration. We show with extensive experiments that
the resulting algorithm achieves better performances for two visual
cues than the classic single cue SVM on the same task, while minimising
the computational load. More important, our method provides a reliable
measure of the level of confidence of the decision.},
URL = {http://www.cognitivesystems.org/publications/pronobis07iros.pdf}
}
@ARTICLE{seemann07cvpr,
AUTHOR = {Edgar Seemann and Mario Fritz and Bernt Schiele},
TITLE = {Towards Robust Pedestrian Detection in Crowded Image Sequences},
JOURNAL = {Computer Vision and Pattern Recognition, IEEE Computer Society Conference
on},
YEAR = {2007},
VOLUME = {0},
PAGES = {1--8},
ABSTRACT = {Object class detection in scenes of realistic complexity remains a
challenging task in computer vision. Most recent approaches focus
on a single and general model for object class detection. However,
in particular in the context of image sequences, it may be advantageous
to adapt the general model to a more object-instance specific model
in order to detect this particular object reliably within the image
sequence. In this work we present a generative object model that
is capable to scale from a general object class model to a more specific
object-instance model. This allows to detect class instances as well
as to distinguish between individual object instances reliably. We
experimentally evaluate the performance of the proposed system on
both still images and image sequences.},
ADDRESS = {Los Alamitos, CA, USA},
PUBLISHER = {IEEE Computer Society},
URL = {http://www.cognitivesystems.org/publications/seemann07cvpr.pdf}
}
@INPROCEEDINGS{Skocaj/etal:2007,
AUTHOR = {D. Sko\v{c}aj and G. Berginc and B. Ridge and A. \v{S}timec and M.
Jogan and O. Vanek and A. Leonardis and M. Hutter and N. Hewes},
TITLE = {A System for Continuous Learning of Visual Concepts},
BOOKTITLE = {International Conference on Computer Vision Systems ICVS 2007},
YEAR = {2007},
ADDRESS = {Bielefeld, Germany},
MONTH = {March},
ABSTRACT = {We present an artifficial cognitive system for learning visual concepts.
It comprises of vision, communication and manipulation sub- systems,
which provide visual input, enable verbal and non-verbal com munication
with a tutor and allow interaction with a given scene. The main goal
is to learn associations between automatically extracted visual features
and words that describe the scene in an open-ended, continuous manner.
In particular, we address the problem of cross-modal learning of
visual properties and spatial relations. We introduce and analyse
several learning modes requiring different levels of tutor supervision.},
URL = {http://www.cognitivesystems.org/publications/skocajICVS07.pdf}
}
@ARTICLE{skocajPR07,
AUTHOR = {D. Sko\v{c}aj and A. Leonardis and H. Bischof},
TITLE = {Weighted and robust learning of subspace representations},
JOURNAL = {Pattern recogn.},
YEAR = {2007},
VOLUME = {40},
PAGES = {1556-1569},
NUMBER = {5},
MONTH = {May},
ABSTRACT = {A reliable system for visual learning and recognition should enable
a selective treatment of individual parts of input data and should
successfully deal with noise and occlusions. These requirements are
not satisfactorily met when visual learning is approached by appearance-based
modeling of objects and scenes using the traditional PCA approach.
In this paper we extend standard PCA approach to overcome these shortcomings.
We first present a weighted version of PCA, which, unlike the standard
approach, considers individual pixels and images selectively, depending
on the corresponding weights. Then we propose a robust PCA method
for obtaining a consistent subspace representation in the presence
of outlying pixels in the training images. The method is based on
the EM algorithm for estimation of principal subspaces in the presence
of missing data. We demonstrate the efficiency of the proposed methods
in a number of experiments.},
URL = {http://www.cognitivesystems.org/publications/skocajPR07.pdf}
}
@INPROCEEDINGS{skocajCVWW07,
AUTHOR = {D. Sko\v{c}aj and B. Ridge and G. Berginc and A. Leonardis},
TITLE = {A Framework for Continuous Learning of Simple Visual Concepts},
BOOKTITLE = {Computer Vision Winter Workshop 2007},
YEAR = {2007},
PAGES = {99-105},
ADDRESS = {St. Lambrecht, Austria},
MONTH = {February},
ABSTRACT = {We present a continuous learning framework for learning simple visual
concepts and its implementation in an artificial cognitive system.
The main goal is to learn associations between automatically extracted
visual features and words that describe the scene in an open-ended,
continuous manner. In particular, we address the problem of cross-modal
learning of elementary visual properties and spatial relations; we
show that the same learning mechanism can be used to both types of
concepts. We introduce and analyse several learning modes requiring
different levels of tutor supervision, ranging from a completely
tutor driven to a completely autonomous exploratory approach.},
URL = {http://www.cognitivesystems.org/publications/skocajCVWW07.pdf}
}
@INPROCEEDINGS{Sloman/etal:2007b,
AUTHOR = {Aaron Sloman},
TITLE = {{Why Some Machines May Need Qualia and How They Can Have Them: Including
a Demanding New Turing Test for Robot Philosophers}},
BOOKTITLE = {{AI and Consciousness: Theoretical Foundations and Current Approaches
AAAI Fall Symposium 2007, Technical Report FS-07-01}},
YEAR = {2007},
EDITOR = {A. Chella and R. Manzotti},
PAGES = {9--16},
ADDRESS = {Menlo Park, CA},
PUBLISHER = {AAAI Press},
NOTE = {http://www.cs.bham.ac.uk/research/projects/cosy/papers/\#tr0705},
ABSTRACT = {This paper extends three decades of work arguing that instead of focusing
only on (adult) human minds, we should study many kinds of minds,
natural and artificial, and try to understand the space containing
all of them, by studying what they do, how they do it, and how the
natural ones can be emulated in synthetic minds. That requires: (a)
understanding sets of requirements that are met by different sorts
of minds, i.e. the niches that they occupy, (b) understanding the
space of possible designs, and (c) understanding the complex and
varied relationships between requirements and designs. Attempts to
model or explain any particular phenomenon, such as vision, emotion,
learning, language use, or consciousness lead to muddle and confusion
unless they are placed in that broader context. in part because current
ontologies for specifying and comparing designs are inconsistent
and inadequate. A methodology for making progress is summarised and
a novel requirement proposed for human-like philosophical robots,
namely that a single generic design, in addition to meeting many
other more familiar requirements, should be capable of developing
different and opposed viewpoints regarding philosophical questions
about consciousness, and the so-called hard problem. No designs proposed
so far come close. },
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-aaai-consciousness.pdf}
}
@INPROCEEDINGS{Sloman:2007a,
AUTHOR = {Aaron Sloman},
TITLE = {{Diversity of Developmental Trajectories in Natural and Artificial
Intelligence}},
BOOKTITLE = {{Computational Approaches to Representation Change during Learning
and Development. AAAI Fall Symposium 2007, Technical Report FS-07-03}},
YEAR = {2007},
EDITOR = {C. T. Morrison and T. Tim Oates},
PAGES = {70--79},
ADDRESS = {Menlo Park, CA},
PUBLISHER = {AAAI Press},
NOTE = {http://www.cs.bham.ac.uk/research/projects/cosy/papers/\#tr0704},
ABSTRACT = {There is still much to learn about the variety of types of learning
and development in nature and the genetic and epigenetic mechanisms
responsible for that variety. This paper is one of a collection exploring
ideas about how to characterise that variety and what AI researchers,
including robot designers, can learn from it. This requires us to
understand important features of the environment. Some robots and
animals can be pre-programmed with all the competences they will
ever need (apart from fine tuning), whereas others will need powerful
learning mechanisms. Instead of using only completely general learning
mechanisms, some robots, like humans, need to start with deep, but
widely applicable, implicit assumptions about the nature of the 3-D
environment, about how to investigate it, about the nature of other
information users in the environment and about good ways to learn
about that environment, e.g. using creative play and exploration.
One feature of such learning could be learning more about how to
learn in that sort of environment. What is learnt initially about
the environment is expressible in terms of an innate ontology, using
innately determined forms of representation, but some learning will
require extending the forms of representation and the ontology used.
Further progress requires close collaboration between AI researchers,
biologists studying animal cognition and biologists studying genetics
and epigenetic mechanisms. },
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-aaai-representation.pdf}
}
@ARTICLE{Sloman/etal:2007,
AUTHOR = {Aaron Sloman and Jackie Chappell},
TITLE = {{Computational Cognitive Epigenetics (Commentary on Jablonka and
Lamb: Evolution in Four Dimensions (2005))}},
JOURNAL = {Behavioral and Brain Sciences},
YEAR = {2007},
VOLUME = {30},
PAGES = {375--6},
NUMBER = {4},
NOTE = {http://www.cs.bham.ac.uk/research/projects/cosy/papers/\#tr0703},
ABSTRACT = {J&L refer only implicitly to aspects of cognitive competence that
preceded both evolution of human language and language learning in
children. These are important for evolution and development but need
to be understood using the 'design-stance', which the book adopts
only for molecular and genetic processes, not for behavioural and
symbolic processes. Design-based analyses reveal more routes from
genome to behaviour than J&L seem to have considered. This both points
to gaps in our understanding of evolution and epigenetic processes,
and may lead to possible ways of filling the gaps. },
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/jablonka-sloman-chappell.pdf}
}
@ARTICLE{stachniss2007it,
AUTHOR = {Cyrill Stachniss and Giorgio Grisetti and \'{O}scar Mart\'{i}nez
Mozos and Wolfram Burgard},
TITLE = {Efficiently Learning Metric and Topological Maps with Autonomous
Service Robots},
JOURNAL = {it--Information Technology},
YEAR = {2007},
VOLUME = {49},
PAGES = {232--237},
NUMBER = {4},
ABSTRACT = {Models of the environment are needed for a wide range of robotic applications,
from search and rescue to automated vacuum cleaning. Learning maps
has therefore been a major research focus in the robotics community
over the last decades. In general, one distinguishes between metric
and topological maps. Metric maps model the environment based on
grids or geometric representations whereas topologicalmaps model
the structure of the environment using a graph. The contribution
of this paper is an approach that learns a metric as well as a topological
map based on laser range data obtained with a mobile robot. Our approach
consists of two steps. First, the robots solves the simultaneous
localization and mapping problem using an efficient probabilistic
filtering technique. In a second step, it acquires semantic information
about the environment using machine learning techniques. This semantic
information allows the robot to distinguish between different types
of places like, e.g., corridors or rooms. This enables the robot
to construct annotated metric as well as topological maps of the
environment. All techniques have been implemented and thoroughly
tested using real mobile robot in a variety of environments.},
ISSN = {1611--2776},
URL = {http://www.cognitivesystems.org/publications/stachniss2007it.pdf}
}
@INPROCEEDINGS{stark07iccv,
AUTHOR = {Michael Stark and Bernt Schiele},
TITLE = {How Good are Local Features for Classes of Geometric Objects},
BOOKTITLE = {Eleventh IEEE International Conference on Computer Vision (ICCV)},
YEAR = {2007},
MONTH = OCT,
NOTE = {Accepted},
ABSTRACT = {Recent work in object categorization often uses local image descriptors
such as SIFT to learn and detect object categories. As such descriptors
explicitly code local appearance they have shown impressive results
on objects with sufficient local appearance statistics. However,
many important object classes such as tools, cups and other man-made
artifacts seem to require features that capture the respective shape
and geometric layout of those object classes. Therefore this paper
compares, on a novel data collection of 10 geometric object classes,
various shape-based features with more appearance based descriptors
such as SIFT. The analysis includes a direct comparison of feature
statistics as well as the results within standard recognition frameworks.
The results suggest that there are indeed differences between shape-
based and more appearance-based features but that those differences
do not always conform with what one might expect.},
LOCATION = {Rio de Janeiro, Brazil},
URL = {http://www.cognitivesystems.org/publications/iccv07.pdf}
}
@INBOOK{triebel2007gfki,
CHAPTER = {Relational Learning in Mobile Robotics: An Application to Semantic
Labeling of Objects in 2D and 3D Environment Maps},
TITLE = {Studies in Classification, Data Analysis, and Knowledge Organization},
YEAR = {2007},
AUTHOR = {Rudolph Triebel and \'{O}scar Mart\'{i}nez Mozos and Wolfram Burgard},
ABSTRACT = {In this paper, we present an algorithm to identify types of places
and objects from 2D and 3D laser range data obtained in indoor environments.
Our approach is a combination of a collective classi cation method
based on associative Markov networks together with an instance-based
feature extraction using nearest neighbor. Additionally, we show
how to select the best features needed to represent the objects and
places, reducing the time needed for the learning and inference steps
while maintaining high classi cation rates. Experimental results
in real data demonstrate the e ectiveness of our approach in indoor
environments.},
URL = {http://www.cognitivesystems.org/publications/triebel2007gfkl_book.pdf}
}
@INPROCEEDINGS{triebel2007ijcai,
AUTHOR = {Triebel, R. and Schmidt, R. and Mart\'{i}nez Mozos, O. and Burgard,
W.},
TITLE = {Instace-based AMN Classification for Improved Object Recognition
in 2D and 3D Laser Range Data},
BOOKTITLE = {Proc.~of the Twentieth International Joint Conference on Artificial
Intelligence (IJCAI)},
YEAR = {2007},
PAGES = {2225--2230},
ADDRESS = {Hyderabad, India},
ABSTRACT = {In this paper, we present an algorithm to identify different types
of objects from 2D and 3D laser range data. Our method is a combination
of an instance-based feature extraction similar to the Nearest-Neighbor
classifier (NN) and a collective classification method that utilizes
associative Markov networks (AMNs). Compared to previous approaches,
we transform the feature vectors so that they are better separable
by linear hyperplanes, which are learned by the AMN classifier. We
present results of extensive experiments in which we evaluate the
performance of our algorithm on several recorded indoor scenes and
compare it to the standard AMN approach as well as the NN classifier.
The classification rate obtained with our algorithm substantially
exceeds those of the AMN and the NN.},
URL = {http://www.cognitivesystems.org/publications/triebel2007ijcai.pdf}
}
@INPROCEEDINGS{urayBMVC07,
AUTHOR = {M. Uray and D. Sko\v{c}aj and P. Roth and H. Bischof and A. Leonardis},
TITLE = {Incremental {LDA} learning by combining reconstructive and discriminative
approaches},
BOOKTITLE = {British machine vision conference 2007},
YEAR = {2007},
PAGES = {272-281},
ABSTRACT = {Incremental subspace methods have proven to enable efficient training
if large amounts of training data have to be processed or if not
all data is available in advance. In this paper we focus on incremental
LDA learning which provides good classification results while it
assures a compact data representation. In contrast to existing incremental
LDA methods we additionally consider reconstructive information when
incrementally building the LDA subspace. Hence, we get a more flexible
representation that is capable to adapt to new data. Moreover, this
allows to add new instances to existing classes as well as to add
new classes. The experimental results show that the proposed approach
outperforms other incremental LDA methods even approaching classification
results obtained by batch learning.},
URL = {http://www.cognitivesystems.org/publications/urayBMVC07.pdf}
}
@INPROCEEDINGS{Zender/etal:2007-roman,
AUTHOR = {H. Zender and P. Jensfelt and G.J.M. Kruijff},
TITLE = {Human- and Situation-Aware People Following},
BOOKTITLE = {Proceedings of the 16th IEEE International Symposium on Robot and
Human Interactive Communication (RO-MAN 2007)},
YEAR = {2007},
ADDRESS = {Jeju Island, Korea},
MONTH = {August},
ABSTRACT = {The paper presents an approach to intelligent, interactive people
following for autonomous robots. The approach combines robust methods
for simultaneous localization and mapping and for people tracking
in order to yield a socially and environmentally sensitive people
following behavior. Unlike current purely reactive approaches ("nearest
point following") it enables the robot to follow a human in a socially
acceptable way, providing verbal and non-verbal feedback to the user
where necessary. At the same time, the robot makes use of information
about the spatial and functional organization of its environment,
so that it can anticipate likely actions performed by a human, and
adjust its motion accordingly. As a result, the robot's behaviors
become less reactive and more intuitive when following people around
an indoor environment. The approach has been fully implemented and
tested. },
URL = {http://www.cognitivesystems.org/publications/zender_etal07-roman_pplfoll.pdf}
}
@INPROCEEDINGS{Zender/etal:2007-AAAI,
AUTHOR = {H. Zender and P. Jensfelt and O. Mart\'{\i}nez Mozos and G.J.M. Kruijff
and W. Burgard},
TITLE = {An Integrated Robotic System for Spatial Understanding and Situated
Interaction in Indoor Environments},
BOOKTITLE = {Proceedings of the Twenty-Second Conference on Artificial Intelligence
(AAAI-07)},
YEAR = {2007},
PAGES = {1584--1589},
ADDRESS = {Vancouver, Canada},
MONTH = {July},
NOTE = {Special Track on Integrated Intelligence},
ABSTRACT = {A major challenge in robotics and artificial intelligence lies in
creating robots that are to cooperate with people in human-populated
environments, e.g. for domestic assistance or elderly care. Such
robots need skills that allow them to interact with the world and
the humans living and working therein. In this paper we investigate
the question of spatial understanding of human-made environments.
The functionalities of our system comprise perception of the world,
natural language, learning, and reasoning. For this purpose we integrate
state-of-the-art components from different disciplines in AI, robotics
and cognitive systems into a mobile robot system. The work focuses
on the description of the principles we used for the integration,
including cross-modal integration, ontology-based mediation, and
multiple levels of abstraction of perception. Finally, we present
experiments with the integrated CoSy Explorer system and list some
of the major lessons that were lea rned from its design, implementation,
and evaluation.},
URL = {http://www.cognitivesystems.org/publications/zender_etal07-aaai_explorer.pdf}
}
@INPROCEEDINGS{Zender/Kruijff:2007,
AUTHOR = {H. Zender and G.J.M. Kruijff},
TITLE = {Multi-Layered Conceptual Spatial Mapping for Autonomous Mobile Robots},
BOOKTITLE = {Proceedings of the Symposium on Intentions in Intelligent Systems},
YEAR = {2007},
ADDRESS = {Stanford University, Palo Alto, CA, USA},
MONTH = {March},
PUBLISHER = {AAAI Spring Symposium Series 2007},
ABSTRACT = {This paper presents an approach to spatial mapping for autonomous
mobile robots that are to operate among, and interact with, non-expert
human users. We argue that our approach of conceptual spatial mapping
helps bridge the gap between the representations needed for low-level
control of the robot, and the conceptual-topological representations
of space humans have. Our approach maintains spatial knowledge on
multiple interconnected layers. We show that a process for map acquisition,
human-augmented mapping, which combines bottom-up and top-down influences
from different modalities, will yield a rich multi-layered spatial
representation. This representation enables the robot to perform
complex actions in a human-populated environment. We show that our
approach can be used to establish a notion of situational and functional
awareness. },
URL = {http://www.cognitivesystems.org/publications/zender_kruijff07-aaaisss.pdf}
}
@INPROCEEDINGS{zender/kruijff:2007-gre,
AUTHOR = {H. Zender and G.J.M. Kruijff},
TITLE = {Towards Generating Referring Expressions in a Mobile Robot Scenario},
BOOKTITLE = {Language and Robots: Proceedings from the Symposium (LangRo'2007)},
YEAR = {2007},
ADDRESS = {Aveiro, Portugal},
MONTH = {December},
ABSTRACT = {This paper describes an approach towards generating referring expressions
that identify and distinguish spatial entities in large-scale space,
e.g. in an office environment, for autonomous mobile robots. In such
a scenario a dialogue is often about things and places outside the
current perceptual fields of the interlocutors. One of the challenges
therefore lies in determining an appropriate dialogue context. Other
important issues are to have adequate models of both the large-scale
spatial environment and of the user's knowledge.},
URL = {http://www.cognitivesystems.org/publications/zender_kruijff07-langro_gre.pdf}
}
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