|
|
chap12.bib
@ARTICLE{Chappell/etal:2007,
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. },
AUTHOR = {Jackie Chappell and Aaron Sloman},
DATE-ADDED = {2009-01-04 19:40:56 +0000},
DATE-MODIFIED = {2009-01-06 09:03:50 +0000},
JOURNAL = {International Journal of Unconventional Computing},
KEYWORDS = {cosy; irlab},
NOTE = {http://www.cs.bham.ac.uk/research/projects/cosy/papers/\#tr0609},
NUMBER = {3},
PAGES = {211--239},
TITLE = {{Natural and artificial meta-configured altricial information-processing
systems}},
VOLUME = {3},
YEAR = {2007},
URL = {http://www.cognitivesystems.org/publications/ijuc.pdf}
}
@TECHREPORT{Sloman:2006a,
ABSTRACT = {Since the 1970s AI as a science has progressively fragmented
into many activities that are very narrowly focused. It is not
clear that work done within these fragments can be combined in
the design of a human-like integrated system -- long held as one
of the goals of AI as science. A strategy is proposed for reintegrating
AI based around a backward-chaining analysis to produce a roadmap
with partially ordered milestones, based on detailed scenarios,
that everyone can agree are worth achieving, even when they disagree
about means.
This is a summary of ideas being developed within the CoSy project about
how to plan long term research using a partially ordered network
of scenarios and a grid of requirements for competences. },
AUTHOR = {A. Sloman},
DATE-ADDED = {2009-01-04 19:55:40 +0000},
DATE-MODIFIED = {2009-01-06 09:03:50 +0000},
EDITOR = {Ion Muslea and Dieter Fox},
INSTITUTION = {University of Birmingham, School of Computer Science},
KEYWORDS = {cosy; irlab},
NOTE = {Poster summary for AAAI'06 Members Poster Session, Boston July
2006. 2-Page abstract at http://www.cs.bham.ac.uk/research/projects/cosy/papers/\#tr0608
Poster at http://www.cs.bham.ac.uk/research/projects/cosy/papers/\#pr0603},
NUMBER = {COSY-TR-0608},
TITLE = {{How to Put the Pieces of AI Together Again}},
YEAR = {2006},
URL = {http://www.cognitivesystems.org/publications/aaai06-member.pdf}
}
@INPROCEEDINGS{Sloman:2006,
ABSTRACT = {This symposium is inspired by UKCRC Research Grand Challenge
5: GC5: Architecture of Brain and Mind.
The aim of GC5 is to provoke unified discussion of long term research
goals in AI, Cognitive Science, and related disciplines, especially
goals concerned with giving computers a useful and general subset
of human capabilities, implemented in a biologically inspired fashion.
The symposium can also be seen as part of a series of related events
attempting to promote a high-level long-term vision of achievable
scientific goals of AI/Cognitive Science, including The DAM (Designing
an Mind) Symposium at AISB'00 (Davis, 2005), the Tutorial on Philosophical
Foundations of AI at IJCAI'01 (Sloman and Scheutz, 2001), the St.
Thomas symposium in 2002 (Minsky et al., 2004), and the IJCAI'05
Tutorial on Learning and Representation in Animals and Robots (Sloman
and Schiele, 2005). It presents themes central to the EC-funded
Cognitive Systems initiative including the CoSy project which is
part of that initiative, whose members have helped to organise
this symposium, and the euCognition project which is funding this
meeting. A common feature is the focus on scientific goals rather
than useful applications though implementation of working systems
is central to the proposed methodology. This introduction to the
symposium provides some background and highlights some of the major
problems to be overcome. },
ADDRESS = {Bristol},
AUTHOR = {A. Sloman},
BOOKTITLE = {{Proceedings of the AISB '06 Adaptation in Artificial
and Biological Systems}},
DATE-ADDED = {2009-01-04 19:55:40 +0000},
DATE-MODIFIED = {2009-01-06 09:03:50 +0000},
KEYWORDS = {cosy; irlab},
MONTH = {April},
NOTE = {http://www.cs.bham.ac.uk/research/projects/cosy/papers/\#tr0602},
TITLE = {{Introduction to Symposium GC5: Architecture of Brain and
Mind Integrating high level cognitive processes with brain
mechanisms and functions in a working robot}},
YEAR = {2006},
URL = {http://www.cognitivesystems.org/publications/sloman-aisb06-gc5-intro.pdf}
}
@INCOLLECTION{Sloman:2008a,
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. },
ADDRESS = {New York},
AUTHOR = {A. Sloman},
BOOKTITLE = {{Cambridge Handbook on Computational Psychology}},
CHAPTER = {26},
DATE-ADDED = {2009-01-04 19:55:40 +0000},
DATE-MODIFIED = {2009-01-06 09:03:50 +0000},
EDITOR = {Ron Sun},
KEYWORDS = {cosy; irlab},
NOTE = {http://www.cs.bham.ac.uk/research/projects/cogaff/07.html\#710},
PAGES = {684--709},
PUBLISHER = {Cambridge University Press},
TITLE = {{Putting the Pieces Together Again}},
YEAR = {2008},
URL = {http://www.cognitivesystems.org/publications/sloman-sunbook.pdf}
}
@ARTICLE{Sloman:2008,
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.},
AUTHOR = {A. Sloman},
DATE-ADDED = {2009-01-04 19:55:40 +0000},
DATE-MODIFIED = {2009-01-06 09:03:50 +0000},
JOURNAL = {Artificial Intelligence},
KEYWORDS = {cosy; irlab},
NOTE = {http://www.cs.bham.ac.uk/research/projects/cosy/papers/\#tr0807},
NUMBER = {18},
PAGES = {2015--2034},
TITLE = {{The Well-Designed Young Mathematician}},
VOLUME = {172},
YEAR = {2008},
EE = {http://dx.doi.org/10.1016/j.artint.2008.09.004},
URL = {http://www.cognitivesystems.org/publications/sloman-aij-08.pdf}
}
@INPROCEEDINGS{Sloman/etal:2005,
ABSTRACT = {Several high level methodological debates among AI researchers,
linguists, psychologists and philosophers, appear to be endless,
e.g. about the need for and nature of representations, about the
role of symbolic processes, about embodiment, about situatedness,
about whether symbol-grounding is needed, and about whether a robot
needs any knowledge at birth or can start simply with a powerful
learning mechanism. Consideration of the variety of capabilities
and development patterns on the precocial-altricial spectrum in
biological organisms will help us to see these debates in a new
light.
It seems that after evolution discovered how to make physical bodies
that grow themselves, it discovered how to make virtual machines
that grow themselves. Researchers attempting to design human-like,
chimp-like or crow-like intelligent robots will need to understand
how. Whether computers as we know them can provide the infrastructure
for such systems is a separate question.
},
ADDRESS = {Edinburgh},
AUTHOR = {A. Sloman and J. Chappell},
BOOKTITLE = {{Proceedings IJCAI'05}},
DATE-ADDED = {2009-01-04 19:55:40 +0000},
DATE-MODIFIED = {2009-01-06 09:03:50 +0000},
KEYWORDS = {cosy; irlab},
NOTE = {http://www.cs.bham.ac.uk/research/cogaff/05.html\#200502},
PAGES = {1187--1192},
PUBLISHER = {IJCAI},
TITLE = {{The Altricial-Precocial Spectrum for Robots}},
YEAR = {2005},
URL = {http://www.cognitivesystems.org/publications/alt-prec-ijcai05.pdf}
}
@BOOK{Sloman/etal:2005c,
ABSTRACT = {A two-day tutorial was held in The University of Edinburgh
on 30th and 31st July 2005 at IJCAI 2005 on REPRESENTATION AND
LEARNING IN ROBOTS AND ANIMALS.},
ADDRESS = {Edinburgh},
DATE-ADDED = {2009-01-04 19:55:40 +0000},
DATE-MODIFIED = {2009-01-06 09:03:50 +0000},
EDITOR = {A. Sloman and B. Schiele},
KEYWORDS = {cosy; irlab},
NOTE = {http://www.cs.bham.ac.uk/research/projects/cosy/conferences},
PUBLISHER = {IJCAI'05},
TITLE = {{Tutorial on Learning and Representation in Animals and Robots}},
YEAR = {2005},
URL = {http://www.cs.bham.ac.uk/research/projects/cosy/conferences/}
}
@TECHREPORT{Sloman:2005,
ABSTRACT = {This discussion note suggests that some forms of expression
that are apparently vague, inviting interpretations of their meaning
in terms of probability distributions, would be better construed as
having a different form of semantics, namely specifying an 'higher
order' function from contexts to truth-conditions. So statements
made using them have a two level semantics. The first level
specifies the function, which has to be applied to arguments
extracted from the context, which may be linguistic or non
linguistic, including the purpose of the communication. Then when
that function is applied to the arguments the result is a
specification of truth-conditions. This can be extended to how
questions and imperatives using those expressions also need to be
interpreted. I first proposed this sort of interpretation for
'better' in 1969 in How to derive 'Better' from 'is', {\em American
Phil. Quarterly} Vol 6, pp43--52, but I think the phenomenon is much
more common than has been realised. I try to show how the use of
such things can be predicted on the basis of Grice's theory of
communication, and draw some conclusions regarding the evolution of
language, and the relations between linguistic and non-linguistic
mental functions. From this viewpoint, communication is
collaborative problem-solving, not the transmission and decoding of
some signal, and the ability to use a language is just a special
case of a more general ability to solve problems by combining
different kinds of competence. This is related to the amazing
invention of a sign language by Nicaraguan deaf children and to
arguments for the evolution of inner structured languages prior to
the evolution of language for communication. This is a discussion
paper and everything is still tentative.},
ADDRESS = {Birmingham, UK},
AUTHOR = {Aaron Sloman},
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},
NUMBER = {COSY-DP-0605},
TITLE = {{Spatial prepositions as higher order functions: And implications
of Grice's theory for evolution of language.}},
TYPE = {Research Note},
YEAR = {2005},
URL = {http://www.cs.bham.ac.uk/research/projects/cosy/papers/spatial-prepositions.html}
}
@INPROCEEDINGS{Sloman:2006b,
ABSTRACT = {Test domains for AI can have a deep impact on research.
The
polyflap domain is proposed for testing complex AI theories
about architectures, mechanisms and forms of representation
involved in features of human and animal intelligence that
evolved to enable perception, action, and learning in diverse
environments containing things that we can perceive and manipulate,
and many complex processes involving objects that
differ in shape, materials, causal properties, and relations to
one another. We need a test environment that is rich enough
to provide some of that variety of structures, processes and
affordances, yet simple enough to be within reach of robotics
research in the not too distant future.},
ADDRESS = {Menlo Park, CA},
AUTHOR = {Aaron Sloman},
BOOKTITLE = {{Position Papers for 2006 AAAI Fellows Symposium}},
DATE-ADDED = {2009-01-04 19:55:40 +0000},
DATE-MODIFIED = {2009-01-06 09:03:50 +0000},
KEYWORDS = {cosy; irlab},
NOTE = {http://www.aaai.org/Fellows/fellows.php and http://www.aaai.org/Fellows/Papers/Fellows16.pdf},
PUBLISHER = {AAAI},
TITLE = {{Polyflaps as a domain for perceiving, acting and learning
in a 3-D world}},
YEAR = {2006},
URL = {http://www.cognitivesystems.org/publications/Fellows16.pdf}
}
@TECHREPORT{Sloman:2006c,
ABSTRACT = { For some decades, researchers in AI and Cognitive Science
have talked about animals or machines as having 'deliberative'
capabilities. In my own work, I have, for 10 years or more, been
contrasting 'reactive', 'deliberative' and 'meta-management' (sometimes
referred to as 'reflective') capabilities (categories within which
many further subdivisions are possible). The key feature of a deliberative
system is the ability to represent and reason about, and to compare
and evaluate, possible situations that do not exist, or are not
known to exist, either because they are future possibilities, or
because they are remote or hypothetical possibilities. That ability
is analysed in more detail in the paper. In particular we see a
need for a fully deliberative system to be able to construct representations
of possible states of affairs of varying structure and varying
complexity, using at least one formalism with compositional semantics,
in mechanisms that allow two or more such structures to be constructed,
analysed and compared, where the result of comparing them may be
another complex structure describing the pros and cons. Additional
related requirements are described.
Much of this is a presentation of old ideas: going back to work by Minsky,
Evans, Winston, and many others during the 1960s and early 1970s.
Although I have taken all that for granted for many years, gradually
I have come to realise that the ideas are not all widely understood
and the word 'deliberative' is used in different ways, partly because
people have not analysed the variety of cases in a deep way that
is widely shared.
I try to contrast 'fully deliberative' systems with much simpler kinds
of 'proto-deliberative' systems, while allowing for many simpler
cases in between (including intermediate states through which evolutionary
trajectories have passed, and some through which developing individuals
may pass). It is important that in a complex architecture with
many components there are different kinds of subsets, and in my
work I have characterised three (partly overlapping) main subsets,
which differ in their evolutionary history, in their spread amongst
other animals besides humans, and in their functionality (though
they may overlap in the kinds of mechanisms they use).
},
ADDRESS = {Birmingham, UK},
AUTHOR = {Aaron Sloman},
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},
MONTH = {May},
NOTE = {http://www.cs.bham.ac.uk/research/projects/cosy/papers/\#dp0604},
NUMBER = {COSY-DP-0604},
TITLE = {{Requirements for a Fully Deliberative Architecture (Or component
of an architecture)}},
TYPE = {Research Note},
YEAR = {2006},
URL = {http://www.cs.bham.ac.uk/research/projects/cosy/papers/fully-deliberative.html}
}
@TECHREPORT{Sloman:2006d,
ABSTRACT = { I have been trying, with limited success, to get people
to understand the importance (for theories of mental processes
including learning, perception, reasoning and communication), of
a distinction between learning about sensorimotor contingencies
(concerned with relations between states, events and processes
within an animal or machine) and learning about objective condition-consequence
contingencies (concerned with relations between states, events
and processes in the environment).
The distinction is important for theories of infant development, for
the design of robots that act in and learn about their environment,
and for philosophical and other theories of embodied cognition.
The document is a discussion note listing some possible reasons why
the different sorts of people fail to appreciate the distinction
(e.g. they are concept empiricists, or they already use the phrase
'sensorimotor' so broadly as to cover both categories, not realising
the importance of the subdivision they are not attending to). Various
examples are presented that illustrate the distinction and its
importance. This elaborates on some of the points made in the discussion
document on 'Orthogonal Recombinable Competences'
},
ADDRESS = {Birmingham, UK},
AUTHOR = {Aaron Sloman},
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},
MONTH = {May},
NOTE = {http://www.cs.bham.ac.uk/research/projects/cosy/papers/\#dp0603},
NUMBER = {COSY-DP-0603},
TITLE = {{Sensorimotor vs objective contingencies}},
TYPE = {Research Note},
YEAR = {2006},
URL = {http://www.cs.bham.ac.uk/research/projects/cosy/papers/sensorimotor.html}
}
@INPROCEEDINGS{Sloman:2007a,
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. },
ADDRESS = {Menlo Park, CA},
AUTHOR = {Aaron Sloman},
BOOKTITLE = {{Computational Approaches to Representation Change during
Learning and Development. AAAI Fall Symposium 2007, Technical Report
FS-07-03}},
DATE-ADDED = {2009-01-04 19:55:40 +0000},
DATE-MODIFIED = {2009-01-06 09:03:50 +0000},
EDITOR = {C. T. Morrison and T. Tim Oates},
KEYWORDS = {cosy; irlab},
NOTE = {http://www.cs.bham.ac.uk/research/projects/cosy/papers/\#tr0704},
PAGES = {70--79},
PUBLISHER = {AAAI Press},
TITLE = {{Diversity of Developmental Trajectories in Natural and Artificial
Intelligence}},
YEAR = {2007},
URL = {http://www.cognitivesystems.org/publications/sloman-aaai-representation.pdf}
}
@TECHREPORT{Sloman:2007e,
ABSTRACT = {Discussion of some of the relationships between (a)
predicting physical, topological and geometrical consequences of
motions and (b) predicting the changes in affordances that result
from such motions, including both (b.1.) changes in {\em action
affordances} (changes in what the agent can do in the environment)
and (b.2.) changes in {\em epistemic affordances}, i.e. changes in
the information available to the agent or changes in the ease of
planning or deciding. It is suggested that in some circumstances the
predictions can be based on processes operating on selected
fragments of a 2-D representation of a 3-D scene (or a 2.5-D
representation when occlusion is involved) and reasoning by
manipulating the representation. Moreover, where uncertainty is a
problem for prediction it is often due to the existence of a ``phase
boundary'' between configurations where the prediction definitely
gives one result and configurations where the prediction definitely
gives another result. One way of reducing uncertainty is move an
object (or even the viewing position) away from such a phase
boundary. This sometimes allows simple, deterministic, geometric
reasoning to be used, instead of much more complex and unreliable
reasoning with probability distributions and expected utilities.},
AUTHOR = {Aaron Sloman},
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},
MONTH = {Nov},
NOTE = {Unpublished discussion paper http://www.cs.bham.ac.uk/research/projects/cosy/papers/\#dp0702
(HTML)},
NUMBER = {COSY-DP-0702},
TITLE = {{Predicting Affordance Changes (Alternatives ways to deal with
uncertainty)}},
YEAR = {2007},
URL = {http://www.cs.bham.ac.uk/research/projects/cosy/papers/changing-affordances.html}
}
@MISC{Sloman:2007d,
ABSTRACT = {Presenting some of the requirements for a truly helpful,
as opposed to merely engaging (or annoying) artificial companion,
with arguments as to why meeting those requirements is way beyond
the current state of the art in AI. },
AUTHOR = {Aaron Sloman},
DATE-ADDED = {2009-01-04 19:55:40 +0000},
DATE-MODIFIED = {2009-01-06 09:03:50 +0000},
INSTITUTION = {University of Birmingham},
KEYWORDS = {cosy; irlab},
MONTH = {October},
NOTE = {Position Paper for Workshop on Artificial Companions in Society:
Perspectives on the Present and Future Organised by the Companions
project. Oxford Internet Institute (25th--26th October, 2007)
http://www.cs.bham.ac.uk/research/projects/cogaff/07.html\#711},
TITLE = {{Requirements for Digital Companions: It's harder than you
think}},
YEAR = {2007},
URL = {http://www.cognitivesystems.org/publications/sloman-oii-2007.pdf}
}
@MISC{Sloman:2007g,
ABSTRACT = {Investigating the evolution of cognition requires an understanding
of how to design working cognitive systems since there is very
little direct evidence (no fossilised behaviours or thoughts).
That claim is illustrated in relation to theories about the evolution
of language. Almost everyone seems to have got things badly wrong
by assuming that language must have started as primitive communication
between individuals that gradually got more complex, and then later
somehow got absorbed into cognitive systems. An alternative theory
is presented here, namely that generalised languages (GLs) supporting
(a) structural variability, (b) compositional semantics (generalised
to include both diagrammatic syntaxes and contextual influences
on semantics at every level) and (c) manipulability for reasoning,
evolved {\em first} for various kinds of 'thinking', i.e. internal
information processing. This is incosistent with many theories
of the evolution of language. It is also inconsistent with Dennett's
account of the evolution of consciousness in {\em Content and Consciousness}
(1969).},
ADDRESS = {Birmingham, UK},
AUTHOR = {Aaron Sloman},
DATE-ADDED = {2009-01-06 08:59:53 +0000},
DATE-MODIFIED = {2009-01-06 09:03:50 +0000},
INSTITUTION = {School of Computer Science, University of Birmingham,},
KEYWORDS = {cosy; irlab},
NOTE = {Presentation given to Birmingham Psychology department. http://www.cs.bham.ac.uk/research/projects/cosy/papers/\#pr0702},
NUMBER = {COSY-PR-0702},
TITLE = {{What evolved first and develops first in children: Languages
for communicating? or Languages for thinking? (Generalised Languages:
GLs)}},
YEAR = {2007},
URL = {http://www.cognitivesystems.org/publications/glang-evo-ai1.pdf}
}
@INPROCEEDINGS{Sloman:2007b,
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.
},
ADDRESS = {Menlo Park, CA},
AUTHOR = {Aaron Sloman},
BOOKTITLE = {{AI and Consciousness: Theoretical Foundations and Current
Approaches AAAI Fall Symposium 2007, Technical Report FS-07-01}},
DATE-ADDED = {2009-01-04 19:55:40 +0000},
DATE-MODIFIED = {2009-01-06 09:03:50 +0000},
EDITOR = {A. Chella and R. Manzotti},
KEYWORDS = {cosy; irlab},
NOTE = {http://www.cs.bham.ac.uk/research/projects/cosy/papers/\#tr0705},
PAGES = {9--16},
PUBLISHER = {AAAI Press},
TITLE = {{Why Some Machines May Need Qualia and How They Can Have Them:
Including a Demanding New Turing Test for Robot Philosophers}},
YEAR = {2007},
URL = {http://www.cognitivesystems.org/publications/sloman-aaai-consciousness.pdf}
}
@MISC{Sloman:2007f,
ABSTRACT = {Introduction to key ideas of semantic models, implicit definitions
and symbol tethering through theory tethering, providing a criticism
concept empiricism, including its recently revived version, ``symbol
grounding theor''. The idea of an axiom system having some models
is explained, showing how the structure of a theory can give some
semantic content to undefined symbols in that theory, making it
unnecessary for all meanings to be derived bottom up from (grounded
in) sensory experience, or sensory-motor contingencies. Although
symbols need not be grounded, since they are mostly defined by
the theory in which they are used, the theory does need to be ``tethered'',
if it is to be capable of being used for predicting and explaining
things that happen, or making plans for acting in the real world.
These ideas were quite well developed by 20th Century philosophers
of science, and I now both attempt to generalise those ideas to
be applicable to theories expressed using non-logical representations
(e.g. maps, diagrams, working models, etc.) and begin to show how
they can be used in explaining how a baby or a robot, can develop
new concepts that have some semantic content but are not definable
in terms of previously understood concepts. There is still much
work to be done, but what needs to be done to explain how intelligent
robots might work, and how humans and other intelligent animals
learn about the environment, is very different from most of what
is going on in robotics and in child and animal psychology. The
addition of new explanatory hypotheses is abduction. Normally abduction
uses pre-existing symbols. The simultaneous introduction of new
symbols and new axioms (ontology-extending abduction) generates
a very difficult problem of controlling search.},
ADDRESS = {Birmingham, UK},
AUTHOR = {Aaron Sloman},
DATE-ADDED = {2009-01-06 09:02:20 +0000},
DATE-MODIFIED = {2009-01-06 09:03:50 +0000},
INSTITUTION = {School of Computer Science, University of Birmingham},
KEYWORDS = {cosy; irlab},
NOTE = {http://www.cs.bham.ac.uk/research/projects/cogaff/talks/\#models},
NUMBER = {COSY-DP-0605},
TITLE = {{Why symbol-grounding is both impossible and unnecessary, and
why theory-tethering is more powerful anyway.}},
TYPE = {Research Note},
YEAR = {2007},
URL = {http://www.cognitivesystems.org/publications/models.pdf}
}
@MISC{Sloman:2008e,
ABSTRACT = {Demonstration that humans can be presented with a
collection of unpredictable photographs of natural, moderately complex
scenes (e.g. about 10), at the rate of one a second, and can then
answer somewhere between 30\% and 70\% of a set of unexpected
questions about what was seen in the pictures. This demonstrates some
constraints on possible mechanisms capable of supporting vision in
humans and perhaps some other animals. The processing needs to go up
several levels of abstraction (e.g. perhaps nine or ten levels) within
a second. This almost certainly makes use of a great deal of prior
knowledge about kinds of things that can be seen in our world, though
most of that knowledge is dormant most of the time. Somehow the image
data can wake up relevant subsets at various levels of abstraction,
which can then collaborate in converging on an interpretation. If the
image is removed after a short time not all the potential processing
will have been completed, but a surprising amount has been achieved.
There seems to be a lot of individual variation, though so far only
informal tests have been done.},
AUTHOR = {Aaron Sloman},
DATE-ADDED = {2009-01-08 10:47:56 +0000},
DATE-MODIFIED = {2009-01-08 10:48:38 +0000},
INSTITUTION = {School of Computer Science, The University of
Birmingham},
KEYWORDS = {cosy; irlab},
NUMBER = {COSY-PR-0801},
TITLE = {{A Multi-picture Challenge for Theories of Vision}},
TYPE = {Research Note},
URL = {http://www.cognitivesystems.org/publications/multipic-challenge.pdf},
YEAR = {2008}
}
@INPROCEEDINGS{Sloman:2008b,
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. },
ADDRESS = {Dagstuhl, Germany},
ANNOTE = {Keywords: Vision, affordances, architectures, development,
design space},
AUTHOR = {Aaron Sloman},
BOOKTITLE = {Logic and Probability for Scene Interpretation },
DATE-ADDED = {2009-01-04 19:55:40 +0000},
DATE-MODIFIED = {2009-01-06 09:03:50 +0000},
EDITOR = {Anthony G. Cohn and David C. Hogg and Ralf M{\"o}ller and
Bernd Neumann},
ISSN = {1862-4405},
KEYWORDS = {cosy; irlab},
NUMBER = {08091},
PUBLISHER = {Schloss Dagstuhl - Leibniz-Zentrum fuer Informatik, Germany},
SERIES = {Dagstuhl Seminar Proceedings},
TITLE = {Architectural and Representational Requirements for Seeing
Processes, Proto-affordances and Affordances},
YEAR = {2008},
URL = {http://www.cognitivesystems.org/publications/08091.SlomanAaron.Paper.1656.pdf}
}
@INPROCEEDINGS{Sloman:2008c,
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.
},
ADDRESS = {Berlin/Heidelberg},
AUTHOR = {Aaron Sloman},
BOOKTITLE = {{Intelligent Computer Mathematics}},
DATE-ADDED = {2009-01-04 19:55:40 +0000},
DATE-MODIFIED = {2009-01-06 09:03:50 +0000},
EDITOR = {Autexier, S. and Campbell, J. and Rubio, J. and Sorge, V.
and Suzuki, M. and Wiedijk, F.},
INSTITUTION = {School of Computer Science, University of Birmingham},
KEYWORDS = {cosy; irlab},
MONTH = {July},
NOTE = {http://www.cs.bham.ac.uk/research/projects/cosy/papers\#tr0802},
PAGES = {558-573},
PUBLISHER = {Springer},
SERIES = {LLNCS no 5144},
TITLE = {{Kantian Philosophy of Mathematics and Young Robots}},
YEAR = {2008},
URL = {http://www.cognitivesystems.org/publications/maths-ai-sloman.pdf}
}
@INPROCEEDINGS{Sloman:2008d,
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.
},
ADDRESS = {Menlo Park, CA},
AUTHOR = {Aaron Sloman},
BOOKTITLE = {{Workshop on Metareasoning, AAAI'08 Conference}},
DATE-ADDED = {2009-01-04 19:55:40 +0000},
DATE-MODIFIED = {2009-01-06 09:03:50 +0000},
EDITOR = {M. T. Cox and A. Raja},
KEYWORDS = {cosy; irlab},
NOTE = {http://www.cs.bham.ac.uk/research/projects/cosy/papers/\#tr0803},
PAGES = {12--20},
PUBLISHER = {AAAI Press},
TITLE = {{Varieties of Meta-cognition in Natural and Artificial Systems}},
YEAR = {2008},
URL = {http://www.cognitivesystems.org/publications/sloman-meta-aaai08.pdf}
}
@INCOLLECTION{Sloman:2009,
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. },
ADDRESS = {London},
ANNOTE = {http://www.cs.bham.ac.uk/research/projects/cosy/papers/#tr0801},
AUTHOR = {Aaron Sloman},
BOOKTITLE = {{Computational Modelling in Behavioural Neuroscience: Closing
the gap between neurophysiology and behaviour.}},
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},
PUBLISHER = {Psychology Press},
TITLE = {{Architectural and representational requirements for seeing
processes and affordances}},
YEAR = {2009},
URL = {http://www.cognitivesystems.org/publications/sloman-newmod.pdf}
}
@INCOLLECTION{Sloman:2009a,
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. },
ADDRESS = {Vienna \& New York},
AUTHOR = {Aaron Sloman},
BOOKTITLE = {{Simulating the Mind: A Technical Neuropsychoanalytical
Approach}},
DATE-ADDED = {2009-01-04 19:55:40 +0000},
DATE-MODIFIED = {2009-01-06 09:03:50 +0000},
EDITOR = {Dietrich, D. and Fodor, G. and Zucker, G. and Bruckner, D.},
KEYWORDS = {cosy; irlab},
NOTE = {http://www.cs.bham.ac.uk/research/projects/cosy/papers/\#tr0702},
PAGES = {124--148},
PUBLISHER = {Springer},
TITLE = {{Machines in the Ghost}},
YEAR = {2009},
URL = {http://www.cognitivesystems.org/publications/sloman-enf07.pdf}
}
@INCOLLECTION{Sloman:2009b,
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.
},
ADDRESS = {Berlin},
AUTHOR = {Aaron Sloman},
BOOKTITLE = {{Creating Brain-like Intelligence}},
DATE-ADDED = {2009-01-04 19:55:40 +0000},
DATE-MODIFIED = {2009-01-06 09:03:50 +0000},
EDITOR = {B. Sendhoff and E. Koerner and O. Sporns and H. Ritter and
K. Doya},
KEYWORDS = {cosy; irlab},
NOTE = {http://www.cs.bham.ac.uk/research/projects/cosy/papers/\#tr0804},
PAGES = {248--277},
PUBLISHER = {Springer-Verlag},
TITLE = {{Some Requirements for Human-like Robots: Why the recent
over-emphasis on embodiment has held up progress}},
YEAR = {2009},
URL = {http://www.cognitivesystems.org/publications/sloman-honda.pdf}
}
@ARTICLE{Sloman/etal:2005a,
ABSTRACT = {It is often thought that there is one key design principle
or at best a small set of design principles, underlying the success
of biological organisms. Candidates include neural nets, `swarm
intelligence', evolutionary computation, dynamical systems, particular
types of architecture or use of a powerful uniform learning mechanism,
e.g. reinforcement learning. All of those support types of self-organising,
self-modifying behaviours. But we are nowhere near understanding
the full variety of powerful information-processing principles
`discovered' by evolution. By attending closely to the diversity
of biological phenomena we may gain key 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. },
AUTHOR = {Aaron Sloman and Jackie Chappell},
DATE-ADDED = {2009-01-04 19:55:40 +0000},
DATE-MODIFIED = {2009-01-06 09:03:50 +0000},
JOURNAL = {AISB Quarterly},
KEYWORDS = {cosy; irlab},
MONTH = {Summer 2005},
NOTE = {http://www.cs.bham.ac.uk/research/cogaff/05.html\#200503},
NUMBER = {121},
PAGES = {5--7},
TITLE = {{Altricial self-organising information-processing systems}},
YEAR = {2005},
URL = {http://www.cognitivesystems.org/publications/summary-gc7.pdf}
}
@ARTICLE{Sloman/etal:2007,
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. },
AUTHOR = {Aaron Sloman and Jackie Chappell},
DATE-ADDED = {2009-01-04 19:55:40 +0000},
DATE-MODIFIED = {2009-01-06 09:03:50 +0000},
JOURNAL = {Behavioral and Brain Sciences},
KEYWORDS = {cosy; irlab},
NOTE = {http://www.cs.bham.ac.uk/research/projects/cosy/papers/\#tr0703},
NUMBER = {4},
PAGES = {375--6},
TITLE = {{Computational Cognitive Epigenetics (Commentary on Jablonka
and Lamb: Evolution in Four Dimensions (2005))}},
VOLUME = {30},
YEAR = {2007},
URL = {http://www.cognitivesystems.org/publications/jablonka-sloman-chappell.pdf}
}
@INPROCEEDINGS{Sloman/etal:2006,
ABSTRACT = {This presentation elaborates on
'The substratum of this experience is the mastery of a technique' (Wittgenstein)
I try to show, with illustrative videos, that many 'techniques' are
implicitly involved in ordinary experiences -- and that the complexities
grow as a child develops, extending its ontology and therefore
the variety of affordances it can experience and use. I point out
that there are two interpretations of sensorimotor contingencies,
one intrasomatic (relating only the contents of sensory and motor
signals at various levels of abstraction) the other extrasomatic
(amodal, objective), referring to an environment that exists independently
of whether and how it is experienced or acted on, and that the
latter provides computational advantages in some cases, supporting
a Kantian rather than a Humean view of knowledge and concepts.
This also suggests a re-interpretation of mirror neurons as 'abstraction
neurons'.
What we are conscious of in the environment depends on the ontology
we have available. A child whose ontology does not include the
notion of boundary, or the notion of alignment of boundaries may
not be able to replace a cut-out wooden picture in its recess,
even if he knows which recess it should go in. Careful observation
of children at various stages shows transitions that involve extensions
of the available ontology, which must go along with development
of suitable forms of representation and mechanisms for manipulating
them, and an architecture that combines them all. Thus the substratum
of the more sophisticated child's experience is mastery of many
'techniques', not just one as implied by Wittgenstein (who probably
did not intend that). It is suggested that there are considerable
differences between precocial species whose competences and architecture
are mostly genetically determined and altricial species that develop
most of their own competences e.g. through playful exploration,
driven by meta-level bootstrapping mechanisms.
Only when I started working in detail on requirements for a human-like
robot able to manipulate 3-D objects using vision and an arm with
gripper did I notice what should have been obvious long before,
namely that structured objects have 'multi-strand' relationships
not expressible simply as R(x, y), because the relation between
x and y involves many relations between parts of x and parts of
y.
For a more detailed presentation of the resulting theory see
COSY-PR-0505: A (Possibly) New Theory of Vision (PDF)
Hence, motion of such structured objects involves 'multi-strand' (concurrent)
processes. That is, many relationships change in parallel -- e.g.
faces, edges, corners of one block may all be changing their relationships
to faces edges and corners of another (and things get more complex
when objects are flexible, e.g. your hand peeling a banana or a
sweater being put on a child).
Thus seeing what you are doing in such cases can have a kind of complexity
that appears not to have been noticed previously because of too
much focus on simpler visual tasks like recognition and tracking.
I'll show why we need to postulate mechanisms in which concurrent processes
at different levels of abstraction, in partial registration with
the optic array (NOT the retina, since saccades, etc., occur frequently)
are represented.
Nothing in AI comes close to modelling this, and it seems likely that
it will be hard to explain in terms of known neural mechanisms.
If the opportunity arises I'll try to explain some of the implications
for human development, understanding of causation, and computational
modelling, and spell out requirements to be addressed in future
interdisciplinary research, explaining deep connections with Gibson's
notion of affordance, and its generalisation to 'vicarious affordance'.
The evolution of grasping devices that move independently of eyes (i.e.
hands instead of mouth or beak) had profound implications -- undermining
claims about sensory-motor contingencies -- also suggesting that
mirror neurons should have been called 'abstraction neurons'.
Some of the ideas are also sketched here: COSY-DP-0601 'Orthogonal Competences
Acquired by Altricial Species'
A critique of common assumptions about 'sensorimotor contingencies'
is presented, including making a distinction between somatic (internal)
and exosomatic (external) ontologies. Too many people expect too
much to come from the somatic (intrasomatic) variety -- including
knowledge of sensorimotor contingencies, a notion criticised in
http://www.cs.bham.ac.uk/research/projects/cosy/papers/#dp0603
Requirements for 'fully deliberative' systems are analysed in http://www.cs.bham.ac.uk/research/projects/cosy/papers/#dp0604
},
ADDRESS = {Internet},
AUTHOR = {Aaron Sloman and Jackie Chappell and The CoSy Team},
BOOKTITLE = {{The tenth annual meeting of the Association for the Scientific
Study of Consciousness, Oxford}},
DATE-ADDED = {2009-01-04 19:55:40 +0000},
DATE-MODIFIED = {2009-01-06 09:03:50 +0000},
KEYWORDS = {cosy; irlab},
MONTH = {June},
NOTE = {http://www.cs.bham.ac.uk/research/projects/cosy/papers/\#pr0602,
Poster for ASSC10, Oxford June 2006. Also at ASSC10 Eprints
Archive: http://eprints.assc.caltech.edu/112/},
PUBLISHER = {ASSC},
TITLE = {{How an animal or robot with 3-D manipulation skills experiences
the world}},
YEAR = {2006},
URL = {http://www.cognitivesystems.org/publications/assc10-poster.pdf}
}
@TECHREPORT{Sloman/etal:2006c,
ABSTRACT = { This is part of an attempt to explain why our ability to
perceive and produce processes involving 3-D objects of varying
shape was so important for the evolution of the human mind, at
the same time as pointing out what is wrong with most of the stuff
that gets written about the importance of embodiment.
I suspect this reinvents some of Piaget's ideas. It is consistent with
some of the main themes of McCarthy's 'The Well-Designed Child'.
I have recently (April 2006) discovered many connections with the
book The Infant's World by Philippe Rochat (2001).
The main idea is that children acquire types of information that are
orthogonal insofar as they relate to aspects of things or situations
in the environment that can vary (nearly) independently, e.g. kinds
of stuff things are made of, kinds of local surface features, kinds
of relations between things, kinds of whole objects (composed of
stuff with specific surfaces and parts with multiple relations),
kinds of processes, etc.
The competences are also recombinable insofar as they can be used in
perceiving or producing novel structures and processes. The requirement
for re-use in novel combinations seems to impose strong requirements
on the forms of representation used. The recombination is predictive:
you can imagine many details of the process of trying to put on
a shirt made of paper, or lead, or the process of sitting on a
chair made of butter, even if you have never encountered such a
thing.
The competences can involve different levels of abstraction.
E.g. grasping something with your teeth and with finger and thumb are
extremely different as regards sensory input and motor signals.
But an animal that can represent what is common to both has a powerful
re-usable abstraction that can also be applied to grasping done
by another person (e.g. a child who may need help) or grasping
done by machines.
I conclude that the so-called 'mirror neurones' should have been called
'abstraction neurones', and that might have prevented much confusion
(e.g. about imitation).
Powerful innate mechanisms are needed for acquiring such competences
through play and exploration. Very few species can do it. As far
as I can tell there is nothing in AI that accounts for this, and
no known neural mechanisms. (Data-mining techniques can be viewed
as deriving separate 'competences' from large amounts of data,
but as far as I know those techniques and the forms of representation
chosen are not designed to support creative recombination, like
solving a problem by inventing something new involving previously
known kinds of motion, of shape, and of physical stuff)
I'd be interested to know if there's anything implemented by anyone
in AI that models such learning. I have not yet found AI literature
identifying the problem, though it's possible that I've read something
in the past which I had forgotten.
It's also likely that someone has used a different label for this notion
of orthogonal competences, which is why I failed to find previous
work on this. },
ADDRESS = {Birmingham, UK},
AUTHOR = {Aaron Sloman and Jackie Chappell and the CoSy PlayMate team},
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},
MONTH = {January},
NOTE = {http://www.cs.bham.ac.uk/research/projects/cosy/papers\#dp0601},
NUMBER = {COSY-DP-0601},
TITLE = {{Orthogonal Recombinable Competences Acquired by Altricial
Species (Blankets, string, and plywood)}},
TYPE = {Research Note},
YEAR = {2006},
URL = {http://www.cs.bham.ac.uk/research/projects/cosy/papers/orthogonal-competences.html}
}
@TECHREPORT{Sloman/etal:2005b,
ABSTRACT = {We report on some of the hard unsolved problems we have
identified on the basis of detailed analysis of some of the
processes that will have to occur when the PlayMate and Explorer
robots perform their tasks. The analysis used our scenario-driven
research methodology. We introduce some preliminary
characterisations of the key problems and some preliminary ideas for
dealing with them, inspired in part by studies of cognition in
humans and other animals. We confirm the conjecture in the CoSy
proposal that various kinds of representations are required for
different sorts of sub-mechanisms (including for instance
representations concerned with planning complex sequences of actions
and representations used in producing and controlling fast and
fluent movements). The different representations are in part related
to different ontologies, since different sub-mechanisms acquire,
manipulate and use information about different subject-matter. A
substantial part of this report is therefore concerned with first
draft, incomplete, ontologies that we expect our robots will need,
some parts of which the robots will have to develop for themselves,
especially ontologies concerned with objects and processes that have
quite complex structures involving multi-strand relationships. A
particularly important requirement for a robot with 3-D manipulation
capabilities is the ability to perceive and understand what we have
labelled 'multi-strand' relationships (where multiple parts of
complex objects are related, e.g. edges, corners and faces of two
cubes), which cause {\em multi-strand processes} to occur when
objects are moved, with several different relationships changing in
parallel. Perceiving such processes seems to require something like
a simulation process to occur. Moreover, this needs to happen at
different levels of abstraction concurrently (some continuous, with
high or low resolution, and some discrete capturing 'qualitative'
structural changes), for the same reason as many researchers have
claimed that perception of static scenes involves multiple-levels of
abstraction. So we conclude that our robot is likely to require an
architecture and mechanisms that support several concurrent
simulations at different levels of abstraction, in registration with
one another and (where appropriate) with the sensory data. It seems
that a mechanism like this can also implement some of what is often
referred to as spatial or visual reasoning, and could be relevant to
perception and understanding of affordances. We consider in
particular requirements for a pre-linguistic robot that is capable
of perceiving, acting in and to some extent reasoning about the
world before being able to talk about it, and raise questions about
how that might relate to learning that adds linguistic competence.
We note that in animals there is wide variation between species that
start with most of the ontology and representational competence they
will ever need and those that somehow learn or develop what they
need and suggest that further study of those cases may yield clues
regarding options for robots of different kinds. Most of this work
has not yet been published. This is work-in-progress and much of it
remains to be expanded, clarified and polished.},
AUTHOR = {Aaron Sloman and Cosy-partners},
DATE-ADDED = {2009-01-04 19:55:40 +0000},
DATE-MODIFIED = {2009-01-06 09:03:50 +0000},
INSTITUTION = {The University of Birmingham, UK},
KEYWORDS = {cosy; irlab},
NOTE = {http://www.cs.bham.ac.uk/research/projects/cosy/papers/\#tr0507},
NUMBER = {COSY-TR-0507},
TITLE = {{CoSy deliverable DR.2.1 Requirements study for representations}},
YEAR = {2005},
URL = {http://cognitivesystems.org/files/dr-02-01-rev1.pdf}
}
@INPROCEEDINGS{Sloman/etal:2006b,
ABSTRACT = {A child or baby robot that has to manipulate 3-D objects
in its environment would face a combinatorial explosion if all
possible situations have to be learnt about separately. This could
take evolutionary time-scales.
It is conjectured that humans and some other altricial species instead
use innate mechanisms for decomposing situations into components
that can be explicitly learnt about, and stored in such a way that
the competence can be re-used in combination with other learnt
competences, in perceiving novel situations and performing novel
actions.
That includes learning about kinds of surface fragments (e.g. varieties
of curvature and surface discontinuities), kinds of surface properties
(e.g. texture, hardness, etc.), kinds of material (rigid, flexible
in different ways), kinds of objects composed of materials and
shapes, kinds of relationships, kinds of changes in relationships,
kinds of causal connections between changes.
These need to be represented in a manner that is independent of precise
sense-data when they are perceived, or sensorimotor contingencies,
so that knowledge about them can be used in planning future actions,
thinking about the past, and comparing actions using different
hands, or hands or mouth in different positions. This implies a
use of 'objective' representations (e.g. of 3-D structure) which
can then also be used in perceiving 'vicarious' affordances (for
others).
An implication is that mirror neurons should have been called 'abstraction
neurons'. There are many other implications, for robotics, psychology
and neuroscience. },
ADDRESS = {Radboud University Nijmegen, NL},
AUTHOR = {Aaron Sloman and Birmingham CoSy Project Team and Jackie Chappell},
BOOKTITLE = {Proceedings CogSys-II},
DATE-ADDED = {2009-01-04 19:55:40 +0000},
DATE-MODIFIED = {2009-01-06 09:03:50 +0000},
EDITOR = {Harold Bekkering},
KEYWORDS = {cosy; irlab},
MONTH = {April},
NOTE = {http://www.cs.bham.ac.uk/research/projects/cosy/papers/\#pr0601,
Conference url: http://www.socsci.ru.nl/CogSys2},
TITLE = {{Poster: Acquiring Orthogonal Recombinable Competences}},
YEAR = {2006},
URL = {http://www.cognitivesystems.org/publications/cogsys2-poster.pdf}
}
@INPROCEEDINGS{Sloman/etal:2006a,
ABSTRACT = {This paper discusses some of the long term objectives of
cognitive
robotics and some of the requirements for meeting those objectives that
are still a very long way off. These include requirements for visual
perception, for architectures, for kinds of learning, and for innate
competences needed to drive learning and development in a variety of
different environments. The work arises mainly out of research on
requirements for forms of representation and architectures within the
PlayMate scenario, which is a scenario concerned with a robot that
perceives, interacts with and talks about 3-D objects on a tabletop,
one
of the scenarios in the EC-funded CoSy Robotics project.},
ADDRESS = {Menlo Park, CA},
AUTHOR = {Aaron Sloman and Jeremy Wyatt and Nick Hawes and Jackie
Chappell and Geert-Jan M. Kruijff},
BOOKTITLE = {{Cognitive Robotics: Papers from the 2006 AAAI Workshop:
Technical Report WS-06-03, http://www.aaai.org/Library/Workshops/ws06-03.php}},
DATE-ADDED = {2009-01-04 19:55:40 +0000},
DATE-MODIFIED = {2009-01-06 09:03:50 +0000},
EDITORS = {Michael Beetz and Kanna Rajan and Michael Thielscher and
Radu Bogdan Rusu},
KEYWORDS = {cosy; irlab},
NOTE = {http://www.cs.bham.ac.uk/research/projects/cosy/papers/\#tr0604},
PAGES = {143--150},
PUBLISHER = {AAAI Press},
TITLE = {{Long Term Requirements for Cognitive Robotics}},
YEAR = {2006},
URL = {http://www.cs.bham.ac.uk/research/projects/cosy/papers/cogrob06.pdf}
}
@MISC{slomanDp0701,
AUTHOR = {Aaron Sloman},
TITLE = {{A First Draft Analysis of some Meta-Requirements for Cognitive
Systems in Robots}},
YEAR = {2007},
NUMBER = {COSY-DP-0701},
NOTE = {Contribution to euCognition wiki, also available as,
http://www.cs.bham.ac.uk/research/projects/cosy/papers/\#dp0701},
ABSTRACT = {This is a contribution to discussions regarding the
construction of a research roadmap for future cognitive systems,
including intelligent robots, in the context of the euCognition
network, and the UKCRC Grand Challenge 5: Architecture of Brain and
Mind. I have argued that in the context of trying either (a) to
produce working systems to elucidate scientific questions about
intelligent systems, or (b) to advance long term engineering
objectives through advancing science, the task of coming up with a
set of requirements that is sufficiently detailed to provide a basis
for developing milestones and evaluation criteria is itself a hard
research problem. One aspect of the problem is to provide an
analysis of words and phrases that are commonly used to specify
objectives, but whose meanings are very abstract and unclear, in
particular words like ``robust''. ``flexible'', ``creative'' and
''autonomous''. This document argues that the words all share a
feature that could be described as expressing a
''meta-requirement''. What that means is that none of them is
directly associated with a set of features which, if found in an
object or process or system, would justify the application of the
label, or which can be used to derive design features. In other
words the words express concepts that do not specify criteria for
their instances though they do express criteria for deriving
criteria. To derive criteria from the concepts more information is
required, from which the criteria can be derived, in a systematic
way that differs for each of the meta-criteria. Analyses of the
words based on this idea are proposed. This is an exercise in
analysis of logical topography. Subsequent work will need to
provided detailed examples of the use of the various meta-criteria.},
URL = {http://www.cs.bham.ac.uk/research/projects/cosy/papers/meta-requirements.html}
}
@MISC{sloman-cosypr0507,
AUTHOR = {Aaron Sloman},
TITLE = {{Perception of structure: Anyone Interested?}},
YEAR = {2005},
NOTE = {http://www.cs.bham.ac.uk/research/projects/cosy/papers/\#pr0507},
NUMBER = {COSY-PR-0507},
TYPE = {Research Note},
INSTITUTION = {School of Computer Science, University of Birmingham},
ADDRESS = {Birmingham, UK},
ABSTRACT = {Illustration of some of the requirements for a vision system
capable
of being used in a robot that manipulates 3-D objects.
The pictures displayed here are very easy for humans to understand not
merely
insofar as they recognise the objects depicted, in spite of poor quality
and
poor resolution, but also because humans easily see various ways in
which the
objects can and cannot be grasped, and can plan a sequence of moves
to
transform one of the configurations presented into another.},
URL = {http://www.cognitivesystems.org/publications/challenge.pdf}
}
@TECHREPORT{cosy-dp-0703,
AUTHOR = {A. Sloman},
YEAR = {2007},
TITLE = {{Two Notions Contrasted: `Logical Geography' and `Logical Topography'
(Variations on a theme by Gilbert Ryle: The logical topography
of `Logical Geography'.)}},
NUMBER = {COSY-DP-0703},
INSTITUTION = {School of Computer Science, University of Birmingham,},
ADDRESS = {Birmingham, UK},
URL = {http://www.cs.bham.ac.uk/research/projects/cosy/papers/#dp0703}
}
This file has been generated by
bibtex2html 1.79
Print this page
|
