a computational framework for concept representation in cognitive systems and architectures:...
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Presentation given at BICA 2014 Conference at Boston, MIT, Massachussets Institute of Technology.TRANSCRIPT
A Computational Framework for Concept Representation in Cognitive Systems and Architectures:
Concepts as Heterogeneous Proxytypes
Antonio Lieto
Dipartimento di Informatica, Università di Torino, Italy
and ICAR-CNR, Italy
[email protected], [email protected]
BICA 2014 Conference, Boston, MA, MIT - Massachussetts Institute of Technology, USA, 7-9 November 2014.
Intro • Proposal of a possible general framework for the representation of
concepts in cognitive systems and architectures.
• The proposal provides a possible bridge between the theoretical and the computational cognitive science concerning the problem of concept representation.
- Theoretical contribution: a novel hypothesis (Concepts as Heterogeneous Proxytypes) providing unexplored connections between different theories of concepts.
- Computational contribution: Description of the computational representational frameworks (and of their interaction) that can be used in order to test/falsify the proposed heterogeneous proxytypes hypothesis.
BICA 2014 Conference, Boston, MA, MIT, Massachussetts Institute of Technology USA, 7-9 November 2014.
Outline
• Concept Representation in Cognitive Science
• Concepts as Heterogeneous Proxytypes
• Related works
• Heterogeneous Proxytypes and Cognitive Architectures
• Tasks and Evaluation (Current and Future work)
BICA 2014 Conference, Boston, MA, MIT, Massachussetts Institute of Technology USA, 7-9 November 2014.
Concept Representation (CR)
In Cognitive Science there were/are different contrasting theories about “how humans represent and organize the information in their mind”.
BICA 2014 Conference, Boston, MA, MIT, Massachussetts Institute of Technology USA, 7-9 November 2014.
Classical Theory
“Knowledge is organized around concepts whose definitions provide necessary and sufficient conditions”.
BICA 2014 Conference, Boston, MA, MIT, Massachussetts Institute of Technology USA, 7-9 November 2014.
Prototype Theory
• The dominant theory of concepts in Psychology, developed by E. Rosch and collaborators in the ’70s.
• categories normally not definable in terms of necessary and sufficient features.
BICA 2014 Conference, Boston, MA, MIT, Massachussetts Institute of Technology USA, 7-9 November 2014.
from Poesio (2013)
BICA 2014 Conference, Boston, MA, MIT, Massachussetts Institute of Technology USA, 7-9 November 2014.
Graded Structure
• Typical items are similar to a prototype
• Typicality effects are naturally predicted
atypical
typical
Influence in Artificial Intelligence
…. Frames, (Minsky M., 1975). Photo from the MIT Museum
Frame 1
Concept 1
Attribute 1 Value 1
Attribute 2 Value 2
Attribute 3 Value 3
… …
BICA 2014 Conference, Boston, MA, MIT, Massachussetts Institute of Technology USA, 7-9 November 2014.
Exemplar Models
• category representation consists of storage of a number of category members
• New exemplars are compared to known exemplars – most similar item will influence classification the most
Multiple Typicality Theories
The different proposals that have been advanced can be grouped in three main classes: a) fuzzy approaches, b) probabilistic and Bayesan approaches, c) approaches based
on non-monotonic formalisms.
Prototype theory: prototypes (an approximate, statistically relevant, representation of a category). A “central” representation of a category.
Exemplar theory: the mental representation of a concept is the set of the representations of (some of) the exemplars of that category that we encountered during our lifetime.
Theory theory: concepts are analogous to theoretical terms in a
scientific theory. For example, the concept CAT is individuated by the role it plays in our mental theory of zoology.
BICA 2014 Conference, Boston, MA, MIT, Massachussetts Institute of Technology USA, 7-9 November 2014.
Multiple «conceptual» representations
The different proposals that have been advanced can be grouped in three main classes: a) fuzzy approaches, b) probabilistic and Bayesan approaches, c) approaches based
on non-monotonic formalisms.
These representations are not mutually exclusive.
Different studies (e.. Starting from Malt, 1989; Smith et al. 97-
98) show that people use different conceptual representations
for dealing with different type of categorization processes.
This aspect represents a first symptom suggesting that concepts
have an heterogeneous nature.
BICA 2014 Conference, Boston, MA, MIT, Massachussetts Institute of Technology USA, 7-9 November 2014.
Heterogeneous Hypothesis
The different proposals that have been advanced can be grouped in three main classes: a) fuzzy approaches, b) probabilistic and Bayesan approaches, c) approaches based
on non-monotonic formalisms.
BICA 2014 Conference, Boston, MA, MIT, Massachussetts Institute of Technology USA, 7-9 November 2014.
It is not necessary that all the different bodies of knowledge are filled
Concepts and Biological Characterization
The different proposals that have been advanced can be grouped in three main classes: a) fuzzy approaches, b) probabilistic and Bayesan approaches, c) approaches based
on non-monotonic formalisms.
Concepts as Proxytypes (Prinz, 2004).
A proxytype is any element of a complex representational network stored in long-term memory corresponding to a particular category that could be tokened in working memory to “go proxy” for that category.
=> Biological characterization.
BICA 2014 Conference, Boston, MA, MIT, Massachussetts Institute of Technology USA, 7-9 November 2014.
Proposal
The different proposals that have been advanced can be grouped in three main classes: a) fuzzy approaches, b) probabilistic and Bayesan approaches, c) approaches based
on non-monotonic formalisms.
Concepts as Heterogeneous Proxytypes:
Heterogeneous proxytypes: heterogeneous hypothesis can be plausibly applied to the idea of concepts as proxytypes (that has been proposed considering concepts as unitary element).
According to this proposal there are multiple representations for a given concept (not just a single one) in Long Term Memory that can “go proxy” for any given percept.
Assumption => What goes proxy is not the whole conceptual information but a particular representation of a concept.
BICA 2014 Conference, Boston, MA, MIT, Massachussetts Institute of Technology USA, 7-9 November 2014.
Ex. Heterogeneous Proxytypes at work
The different proposals that have been advanced can be grouped in three main classes: a) fuzzy approaches, b) probabilistic and Bayesan approaches, c) approaches based
on non-monotonic formalisms.
BICA 2014 Conference, Boston, MA, MIT, Massachussetts Institute of Technology USA, 7-9 November 2014.
From a Computational Perspective
Concepts (more precisely, their computational representation) as composed by different frameworks each one specialized for dealing with specific representational and reasoning aspects of the conceptual level.
Different types of representations (e.g. symbolic solutions, conceptual spaces and artificial neural networks, ANN) can be combined and integrated in order to represent different semantic aspects of the same conceptual entity.
BICA 2014 Conference, Boston, MA, MIT, Massachussetts Institute of Technology USA, 7-9 November 2014.
From a Computational Perspective
• Classical representations: supported by standard symbolic frameworks (e.g. KL-ONE systems and, nowadays, formal ontologies).
• Prototypical representations: E.g. Frames (simbolic); geometric frameworks: Conceptual Spaces (Gärdenfors 2000 and 2014); reinforced patterns of connections emerging according to classical Hebbian mechanisms in artificial neural networks (ANN).
• Exemplar-based representations: as instances of a concept in symbolic systems, as points in a conceptual space or as a particular pattern of activation in a ANN.
• Theory-theory representations: symbolic and conceptual level
allow a most promising descriptive way w.r.t. the sub-symbolic one.
BICA 2014 Conference, Boston, MA, MIT, Massachussetts Institute of Technology USA, 7-9 November 2014.
Proxyfication
“Proxyfication”: process that allows to tokenize the computational conceptual representation in working memory (e.g. in a cognitive architecture, in a complex cognitive system…).
Different computational mechanisms for “proxyfying” the conceptual representations can be applied.
The simplest version can be obtained, for example, by implementing IF-THEN rules able to activate the working memory tokenization of a given representation (e.g. based on a similarity threshold) but other methods can be hypothesized.
BICA 2014 Conference, Boston, MA, MIT, Massachussetts Institute of Technology USA, 7-9 November 2014.
Related works
Sowa 2012
Difference: in our framework, different proxytypes C can be activated each corresponding to different representations in the long term memory. In addition, according to the tokenized representation in the working memory, different neurocognitive “conceptual” networks can be activated which are functions of the activated representation.
BICA 2014 Conference, Boston, MA, MIT, Massachussetts Institute of Technology USA, 7-9 November 2014.
Semantic Pointers
21
Difference: their focus is on sensory channels. Our focus is on the heterogeneity regarding the content of the represented information. The content is cross-channel.
BICA 2014 Conference, Boston, MA, MIT, Massachussetts Institute of Technology USA, 7-9 November 2014.
Thagard 2012 and Eliasmith et al. 2012
What we want to do…
• We want to test the hypothesis of the heterogeneous proxytypes in tasks of concept identification and retrieval.
E.g. Given a perceptual stimulus (verbal or not verbal) we should be able to predict the activated corresponding conceptual representation (as for the humans). Is this hypothesis plausible for explaining psychological results of conceptual categorization ? Comparison of the obtained results with human answers. • We want to implement such representational hypothesis in the Knowledge
Model of Cognitive Architectures (why? Testing of different neural disfunctions in buffer, proxyfications etc., access to LTM...).
• Different Cognitive Architectures could provide different answers.
BICA 2014 Conference, Boston, MA, MIT, Massachussetts Institute of Technology USA, 7-9 November 2014.
What we are doing…
The different proposals that have been advanced can be grouped in three main classes: a) fuzzy approaches, b) probabilistic and Bayesan approaches, c) approaches based
on non-monotonic formalisms.
Extending Cognitive Architectures with such knowledge representation hypothesis.
Which Cognitive Architectures ?
- ACT-R (current work)
- ….
BICA 2014 Conference, Boston, MA, MIT, Massachussetts Institute of Technology USA, 7-9 November 2014.
Proxytypes in ACT-R
The different proposals that have been advanced can be grouped in three main classes: a) fuzzy approaches, b) probabilistic and Bayesan approaches, c) approaches based
on non-monotonic formalisms.
Heterogeneous representations
proxyfication
BICA 2014 Conference, Boston, MA, MIT, Massachussetts Institute of Technology USA, 7-9 November 2014.
Future work
The different proposals that have been advanced can be grouped in three main classes: a) fuzzy approaches, b) probabilistic and Bayesan approaches, c) approaches based
on non-monotonic formalisms.
Test the heterogeneous proxytypes hypothesis in tasks of
conceptual categorization with multiple representations (in
ACT-R).
Evaluate the categorization results and their alignment with the
psychological data in terms of exemplars/prototype based
categorization.
Considering this representational framework in different
cognitive architectures.
BICA 2014 Conference, Boston, MA, MIT, Massachussetts Institute of Technology USA, 7-9 November 2014.
Future work
The different proposals that have been advanced can be grouped in three main classes: a) fuzzy approaches, b) probabilistic and Bayesan approaches, c) approaches based
on non-monotonic formalisms.
Which Cognitive Architectures ?
- ACT-R (current work)
- CLARION (future work)
- SOAR (future work)
- PSI (future work)
- SIGMA (future work)
- ….
BICA 2014 Conference, Boston, MA, MIT, Massachussetts Institute of Technology USA, 7-9 November 2014.
The different proposals that have been advanced can be grouped in three main classes: a) fuzzy approaches, b) probabilistic and Bayesan approaches, c) approaches based
on non-monotonic formalisms.
Thanks for your attention !
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