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

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