Modeling Mental Contexts and Their Interactions Wei Chen and Scott Fahlman Carnegie Mellon University.

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Modeling Mental Contexts and Their Interactions Wei Chen and Scott Fahlman Carnegie Mellon University Slide 2 What is the problem? To represent mental states expressed in natural language – Mental states: belief, knowledge, intention, supposition, perceived reality, etc. Example: “The girl was surprised to find her grandmother’s cottage door open” 1. There is a belief change 2. What is true in the girls old belief and new belief 3. The reality is different from the girl’s old belief Slide 3 Properties of Mental States Nested “The police believe the thieves were trying to steal a solar panel” Change with time “realize”, “remind”, “be surprised”, “forget”, etc. Interactions between mental states “She wants to find her father because she believes he is still alive” Slide 4 How to solve the problem? Mental Context Network Implemented in Scone KB Context activation mechanism Slide 5 Properties of Mental Context Network Mental contexts inherit from a context that holds a set of background knowledge. Mental contexts evolve with time Mental contexts are connected to events. Mental contexts are environment-sensitive. Slide 6 Representing Dormant Memory “remember” and “forget” X X belie f “P forgets X” Before ContextAfter Context person P dormant knowledge context XXXX After Context Before Context “P remembers X” person P X Slide 7 Inter-contextual Activities Default inference rules – E.g. when a conflict is detected between the perceived reality and mental contexts, build new beliefs according to the perceived reality LRC’s old belief LRC’s new belief The cottage door is open reality The cottage door is closed Conflict detected Negate The cottage door is closed Slide 8 Applications Subjectivity Analysis Question answering Question generation Slide 9 Conclusion and Future Work Contributions: – A multi-mental-context network that represents various mental states – An inter-contextual inference mechanism which performs reasoning based on new information and a multi-modal memory Future Works: – Scaling up to causal relations, temporal information, conditional statements etc.

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