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Thomas Hinrichs

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3 papers

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AAAI Conference 2014 Conference Paper

Using Narrative Function to Extract Qualitative Information from Natural Language Texts

Clifton McFate
Kenneth Forbus
Thomas Hinrichs

The naturalness of qualitative reasoning suggests that qualitative representations might be an important component of the semantics of natural language. Prior work showed that framebased 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 higherorder 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.

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AAAI Conference 2012 Conference Paper

Learning Qualitative Models by Demonstration

Thomas Hinrichs
Kenneth Forbus

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.

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IS Journal 2009 Journal Article

Companion Cognitive Systems: Design Goals and Lessons Learned So Far

Kenneth D. Forbus
Matthew Klenk
Thomas Hinrichs

The companion cognitive architecture supports experiments in achieving human-level intelligence. This article describes seven key design goals of companions, relating them to properties of human reasoning and learning, and to engineering concerns raised by attempting to build large-scale cognitive systems. We summarize our experiences with companions in two domains: test taking and game playing.

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