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Formalizations of Commonsense Psychology

Authored By: Andrew Gordon & Jerry Hobbs Presented By: G. Ryan Anderson. Formalizations of Commonsense Psychology. Introduction to Commonsense Psychology . Concerns all of the aspects of the way people think they think

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Formalizations of Commonsense Psychology

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  1. Authored By: Andrew Gordon & Jerry Hobbs Presented By: G. Ryan Anderson Formalizations of Commonsense Psychology

  2. Introduction to Commonsense Psychology • Concerns all of the aspects of the way people think they think • Plans, goals, threats, emotions, memories, and all other mental states that can exist • Central in our ability to reason and make deep inferences • More informed by the field of cognitive psychology than by AI

  3. How could this be useful to AI? • Systems that can successfully reason about people are likely to be much more useful than systems grounded in the natural sciences

  4. How can this be used in AI? • Formal axioms for commonsense knowledge representation • Theories with adequate competence • Theories with adequate coverage

  5. Representational Requirements • Group aspects and characteristics of various domains into a manageable set of representational areas • 48 total representational areas

  6. Representational Requirements • Sample set of representational areas below • Representation somewhat incomplete, and more elaboration is necessary

  7. Using Natural Language for Commonsense Representation • Natural language is very effective in making conceptual distinctions • Language-based methodology for elaborating on the previously mentioned representational areas

  8. Step One: Expression Elicitation • Acquire an initial set of words, expressions, and sentences used to relate to a given representational area

  9. Step Two: Lexical Expansion • Take our set of expressions from step one • Search for related words and expressions, using linguistic resources • Builds up the quantity of expressions for a given area, thus giving a deeper degree of coverage

  10. Step Three: Corpus Analysis • Collection of a large database of examples of language use in the representational area

  11. Step Four: Model Building • Review the results of step three to understand the distinctions made in real language use • Clustering of sentences, words, and other expressions into sets where they are synonymously used

  12. Building a Commonsense Theory of Memory • Having built a set of representational constructs, we can now axiomize our data into formal theories • Memory Retrieval is one of the 48 areas mentioned earlier

  13. Concepts in Memory

  14. Accessibility of Concepts

  15. Association of Memories

  16. Association of Memories (cont.) • Example of formal axiom for concept association

  17. Other Aspects of Memory • Remembering • Forgetting • Repressing

  18. Conclusions • Development of theories with adequate coverage and competency • Sorted into manageable amount of domains • Elaborate on domains using natural language tactics • Results can be moulded into more formal axioms

  19. Conclusions (cont.) • Memory is one of 48 representational areas • Challenge lies in integrating all 48 areas • Overall goal is to construct AI systems that have a solid representation of human commonsense models to more effectively reason the way humans do

  20. Questions? • Paper can be found at http://people.ict.usc.edu/~gordon/AIMAG04.PDF

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