Institute for Creative Technologies

Representations of an AI agent’s mental states and processes are necessary to enable metareasoning, i.e., thinking about thinking. However, the formulation of suitable representations remains an outstanding AI research challenge, with no clear consensus on how to proceed. This paper outlines an approach involving the formulation of anthropomorphic self-models, where the representations that are used for metareasoning are based on formalizations of commonsense psychology. We describe two research activities that support this approach, the formalization of broad-coverage commonsense psychology theories and use of representations in the monitoring and control of object level reasoning. We focus specifically on metareasoning about memory, but argue that anthropomorphic self-models support the development of integrated, reusable, broadcoverage representations for use in metareasoning systems.

We understand discourse so well because we know so much. If we are to have natural language understanding systems that are able to deal with texts with emotional content, we must encode knowledge of human emotions for use in the systems. In particular, we must equip the system with a formal version of people’s implicit theory of how emotions mediate between what they experience and what they do, and rules that link the theory with words and phrases in the emotional lexicon. The effort we describe here is part of a larger project in knowledge-based natural language understanding to construct a collection of abstract and concrete core formal theories of fundamental phenomena, geared to language, and to define or at least characterize the most common words in English in terms of these theories (Hobbs, 2008). One collection of theories we have put a considerable amount of work into is a commonsense theory of human cognition, or how people think they think (Hobbs and Gordon, 2005). A formal theory of emotions is an important piece of this. In this paper we describe this theory and our efforts to define a number of the most common words about emotions in terms of this and other theories. Vocabulary related to emotions has been studied extensively within the field of linguistics, with particular attention to cross-cultural differences (Athanasiadou and Tabakowska, 1998; Harkins and Wierzbicka, 2001; Wierzbicka, 1999). Within computational linguistics, there has been recent interest in creating large-scale text corpora where expressions of emotion and other private states are annotated (Wiebe et al., 2005). In Section 2 we describe Core WordNet and our categorization of it to determine the most frequent words about cognition and emotion. In Section 3 we describe an effort to flesh out the emotional lexicon by searching a large corpus for emotional terms, so we can have some assurance of high coverage in both the core theory and the lexical items linked to it. In Section 4 we sketch the principal facets of some of the core theories. In Section 5 we describe the theory of Emotion with several examples of words characterized in terms of the theories.

Increasingly, the military has requirements for teaching cultural awareness, which demands flexible representations of cultural knowledge. The Culturally-Affected Behavior project seeks to define a language for encoding ethnographic data in order to capture cultural knowledge and use that knowledge to affect human behavior models. Having anthropologists encode ethnographic data will validate the language and will result in a library of culture models for immersive training.