Many applications of natural language processing technologies involve analyzing texts that concern the psychological states and processes of people, including their beliefs, goals, predictions, explanations, and plans. In this paper, we describe our efforts to create a robust, large-scale lexical-semantic resource for the recognition and classification of expressions of commonsense psychology in English Text. We achieve high levels of precision and recall by hand-authoring sets of local grammars for commonsense psychology concepts, and show that this approach can

The utility of formal theories of commonsense reasoning will depend both on their competency in solving problems and on their conceptual coverage. We argue that the problems of coverage and competency can be decoupled and solved with different methods for a given commonsense domain. We describe a methodology for identifying the coverage requirements of theories through the large-scale analysis of planning strategies, with further refinements made by collecting and categorizing instances of natural language expressions pertaining to the domain. We demonstrate the effectiveness of this methodology in identifying the representational coverage requirements of theories of the commonsense psychology of human memory. We then apply traditional methods of formalization to produce a formal first-order theory of commonsense memory with a high degree of competency and coverage.

Robust intelligent systems will require a capacity for metacognitive reasoning, where intelligent systems monitor and reflect on their own reasoning processes. A large-scale study of human strategic reasoning indicates that rich representational models of commonsense psychology are available to enable human metacognition. In this paper, we argue that large-scale formalizations of commonsense psychology enable metacognitive reasoning in intelligent systems. We describe our progress toward developing 30 integrated axiomatic theories of commonsense psychology, and discuss the central representational challenges that have arisen in this work to date. Commonsense Psychology and Metacognitive Reasoning

Metacognitive reasoning in computational systems will be enabled by the development of formal theories that have broad coverage over mental states and processes as well as inferential competency. In this paper we evaluate the inferential competency of an existing formal theory of commonsense human memory by attempting to use it to validate the appropriateness of a commonsense memory strategy. We formulate a particular memory strategy (to create an associated obstacle) as a theorem in first-order predicate calculus. We then attempt to validate this strategy by showing that it is entailed by the axioms of the theory we evaluated. These axioms were encoded into the syntax of an automated reasoning system, which was used to automatically generate inferences and search for formal

Children develop certain abilities related to Theory of Mind reasoning, particularly concerning the False-belief Task, between the ages of 3 and 5. This paper investigates whether there is a corresponding change in the frequency of linguistic expressions related to knowledge and belief produced by children around these ages. Automated corpus analysis techniques are used to tag each expression related to knowledge and belief in a large corpus of transcripts of speech from normally developing English-learning children.

In the context of developing formal theories of commonsense psychology, or how peole think they think, we have developed a formal theory of goals. In it we explicate and axiomatize, among others, the goal-related notions of trying, success, failure, functionality, intactness, and importance.