The naturalness of qualitative reasoning suggests that qualitative representations might be an important component of the semantics of natural language. Prior work showed that frame-based representations of qualitative process theory constructs could indeed be extracted from natural language texts. That technique relied on the parser recognizing specific syntactic constructions, which had limited coverage. This paper describes a new approach, using narrative function to represent the higher-order relationships between the constituents of a sentence and between sentences in a discourse. We outline how narrative function combined with query-driven abduction enables the same kinds of information to be extracted from natural language texts. Moreover, we also show how the same technique can be used to extract type-level qualitative representations from text, and used to improve performance in playing a strategy game.

Creating software agents that learn interactively requires the ability to learn from a small number of trials, extracting general, flexible knowledge that can drive behavior from observation and interaction. We claim that qualitative models provide a useful intermediate level of causal representation for dynamic domains, including the formulation of strategies and tactics. We argue that qualitative models are quickly learnable, and enable model-based reasoning techniques to be used to recognize, operationalize, and construct more strategic knowledge. This paper describes an approach to incrementally learning qualitative influences by demonstration in the context of a strategy game. We show how the learned model can help a system play by enabling it to explain which actions could contribute to maximizing a quantitative goal. We also show how reasoning about the model allows it to reformulate a learning problem to address delayed effects and credit assignment, such that it can improve its performance on more strategic tasks such as city placement.

Our goal is to enable military planners to rapidly critique alternative battle plans by simulating multiple outcomes of adversarial plans. We describe a novel simulator, SimPath, that combines qualitative reasoning, a geographic information system (GIS), and targeted probabilistic calculations to envision how adversarial battle plans can play out. We outline the problem and describe the overall operation of the simulator. We then explain how qualitative process theory is extended with actions to model military tasks, how envisioning is factored to reduce combinatorial explosions, and how probabilities are computed for transitions and used to filter possibilities. Empirical results, including an experiment conducted by an independent evaluator, are summarized. The results show that it is possible to identify dozens of possible outcomes on each of 9 combinations of adversarial plans (COAs) in under two minutes. We close with a discussion of future work.