This document proposes a framework for representing concepts in cognitive systems called "concepts as heterogeneous proxytypes". It suggests concepts have multiple representations, including classical, prototypical, exemplar-based and theory-based. These representations are stored separately but can be combined. The framework represents concepts computationally using different frameworks like symbols, conceptual spaces and neural networks. It aims to test if this heterogeneous proxytype hypothesis can explain human concept identification and retrieval by implementing it in cognitive architectures.
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The Foundations of Artificial Intelligence, The History of
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The Foundations of Artificial Intelligence, The History of
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problem-solving Agents, Formulating problems, Example problems, and searching for Solutions,
Search Strategies, Avoiding Repeated States, and Constraint Satisfaction Search. Informed
Search Methods: Best-First Search, Heuristic Functions, Memory Bounded Search, and Iterative
Improvement Algorithms.
Artificial intelligence - Approach and MethodRuchi Jain
Human natural intelligence is ubiquitous with human activities, such as solving problems, playing chess, guessing puzzles. AI is new mean to solve such complex problems. We NuAIg is a AI consulting firm, who will help you to create a AI road-map for your business and process automation.
Extending and integrating a hybrid knowledge representation system into the c...Valentina Rho
Extending and integrating a hybrid knowledge representation system into the cognitive architecture ACT-R - 15th International Conference of the Italian Association for Artificial Intelligence - 1 December 2016
Human-robot interaction can increase the challenges of artificial intelligence. Many domains of AI and its effect is laid down, which is mainly called for their integration, modelling of human cognition and human, collecting and representing knowledge, use of this knowledge in human level, maintaining decision making processes and providing these decisions towards physical action eligible to and in coordination with humans. A huge number of AI technologies are abstracted from task planning to theory of mind building, from visual processing to symbolic reasoning and from reactive control to action recognition and learning. Specific human-robot interaction is focused on this case. Multi-model and situated communication can support human-robot collaborative task achievement. Present study deals with the process of using artificial intelligence (AI) for human-robot interaction. by Vishal Dineshkumar Soni 2018. Artificial Cognition for Human-robot Interaction. International Journal on Integrated Education. 1, 1 (Dec. 2018), 49-53. DOI:https://doi.org/10.31149/ijie.v1i1.482. https://journals.researchparks.org/index.php/IJIE/article/view/482/459 https://journals.researchparks.org/index.php/IJIE/article/view/482
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Artificial intelligence uses in productive systems and impacts on the world...Fernando Alcoforado
This essay aims to present the scientific and technological advances of artificial intelligence, their uses in productive systems and their impacts in the world of work.
Extending and integrating a hybrid knowledge representation system into the c...Valentina Rho
Extending and integrating a hybrid knowledge representation system into the cognitive architecture ACT-R - 15th International Conference of the Italian Association for Artificial Intelligence - 1 December 2016
Human-robot interaction can increase the challenges of artificial intelligence. Many domains of AI and its effect is laid down, which is mainly called for their integration, modelling of human cognition and human, collecting and representing knowledge, use of this knowledge in human level, maintaining decision making processes and providing these decisions towards physical action eligible to and in coordination with humans. A huge number of AI technologies are abstracted from task planning to theory of mind building, from visual processing to symbolic reasoning and from reactive control to action recognition and learning. Specific human-robot interaction is focused on this case. Multi-model and situated communication can support human-robot collaborative task achievement. Present study deals with the process of using artificial intelligence (AI) for human-robot interaction. by Vishal Dineshkumar Soni 2018. Artificial Cognition for Human-robot Interaction. International Journal on Integrated Education. 1, 1 (Dec. 2018), 49-53. DOI:https://doi.org/10.31149/ijie.v1i1.482. https://journals.researchparks.org/index.php/IJIE/article/view/482/459 https://journals.researchparks.org/index.php/IJIE/article/view/482
Computer Aided Development of Fuzzy, Neural and Neuro-Fuzzy SystemsIJEACS
Development of an expert system is difficult because of two challenges involve in it. The first one is the expert system itself is high level system and deals with knowledge, which make is difficult to handle. Second, the systems development is more art and less science; hence there are little guidelines available about the development. This paper describes computer aided development of intelligent systems using modem artificial intelligence technology. The paper illustrates a design of a reusable generic framework to support friendly development of fuzzy, neural network and hybrid systems such as neuro-fuzzy system. The reusable component libraries for fuzzy logic based systems, neural network based system and hybrid system such as neuro-fuzzy system are developed and accommodated in this framework. The paper demonstrates code snippets, interface screens and class libraries overview with necessary technical details.
Artificial intelligence uses in productive systems and impacts on the world...Fernando Alcoforado
This essay aims to present the scientific and technological advances of artificial intelligence, their uses in productive systems and their impacts in the world of work.
Extending the knowledge level of cognitive architectures with Conceptual Spac...Antonio Lieto
Extending the knowledge level of cognitive architectures with Conceptual Spaces (+ a case study with Dual-PECCS: a hybrid knowledge representation system for common sense reasoning). Talk given at Stockholm, September 2016.
Development of a Conceptual Space Smart Kitchen MixerMarlene Holm
The authors and designers Marlene Holm and Olof Nordström, have developed a product concept based on the former kitchen mixer Electrolux N15, that fills the gap in the product segment that is referred to as ’Urban Compact Living’, in which there is currently no equivalent product offered.
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Two stand mixers of the wall mounted model Electrolux N15, with all the attachments and manuals, was purchased and forms the base for making the practical tests and studies.
Much of the work consists of usability analyzes in order to understand what users want, both what they consciously seek but also implicit needs; what they do not yet know that they want. User studies are conducted where users are observed and the product’s usability is analyzed with the help of a CW, -Cognitive Walkthrough and a PHEA, -Predicted Human Error Analysis test.
Electrolux's design dna is analyzed to create a product that exudes their visual brand identity and meet the customer expectations. The idea generation is an iterative process where creative tools such as brainstorming, exploratory sketching and clay modelling are used to create the concept, its features and final design. To further develop and visualize, the concept is 3D-modeled in CAD software and finally rendered into photo realistic images. A full scale prototype is made using a 3D printer to evaluate the shape and the size.
The result is an innovative, user friendly and time saving stand mixer that inspires people with an urban lifestyle and a compact living to a true and wholesome cooking experience.
This is a 30 credit Master Thesis for the MSc Industrial Design Engineer program.
Conceptual Spaces for Cognitive Architectures: A Lingua Franca for Different ...Antonio Lieto
We claim that Conceptual Spaces offer a lingua franca that allows to unify and generalize many aspects of the symbolic, sub-symbolic and diagrammatic approaches (by overcoming some of their typical problems) and to integrate them on a common ground. In doing so we extend and detail some of the arguments explored by Gardenfors [23] for defending the need of a conceptual, intermediate, representation level between
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reasoning in Cognitive Architectures
In this presentation, Sushma describes an innovative approach that can be used to automate directions given to the blind. Sushma's interest area lies in the same domain.
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Presents Natural Language Processing (NLP) algorithms for for Bay Area NLP reading group. Survey of Probabilistic Topic Modeling such as Latent Dirichlet Allocation (LDA). Includes practical references explaining the algorithm along with software libraries for Python, Spark, and R.
Comparison of methods – an unloved duty? Examples from an ongoing bibliometri...Andrea Scharnhorst
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A Computational Framework for Concept Representation in Cognitive Systems and Architectures: Concepts as Heterogeneous Proxytypes - BICA 2014 - MIT
1. 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
lieto@di.unito.it, alieto@acm.org
BICA 2014 Conference, Boston, MA, MIT - Massachussetts Institute of Technology, USA, 7-9 November 2014.
2. 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.
3. 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.
4. 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.
5. 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.
6. 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.
7. from Poesio (2013)
BICA 2014 Conference, Boston, MA, MIT, Massachussetts Institute of Technology USA, 7-9 November 2014.
8. Graded Structure
•Typical items are similar to a prototype
•Typicality effects are naturally predicted
atypical
typical
9. 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.
10. 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
11. 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.
12. 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.
13. 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
14. 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.
15. 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.
16. 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.
17. 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.
18. 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.
19. 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.
20. 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.
21. 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
22. 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.
23. 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.
24. 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.
25. 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.
26. 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.
27. 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 ! Contacts: lieto@di.unito.it alieto@acm.org lieto.antonio@gmail.com