On a mechanical reasoning about causal relations

Fusaoka, Akira; Takahashi, Kazuhiko

doi:10.1016/0954-1810(86)90030-0

Cited by 5 publications

(3 citation statements)

References 3 publications

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“…Kosko [112] views causality as a fuzzy relation on causal concepts and defines a causal algebra to traverse a fuzzy cognitive map to find causal relationships. Fusaoka and Takahashi [85] present a method for representing and reasoning about progressive causation and regressive causation, defined earlier, using modal logic. The model is based on a tree structure of possible worlds.…”

Section: Causality In Artificial Intelligencementioning

confidence: 99%

Contributions to a Computer-Based Theory of Strategies

Findler

1990

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Section: Causality In Artificial Intelligencementioning

confidence: 99%

Contributions to a Computer-Based Theory of Strategies

Findler

1990

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“…Studies of causation in artificial intelligence (AI) have primarily focused on statistical (Charniak & Goldman, 1989;Gabrielian & Stickney, 1987;Kim 8c Pearl, 1983;Liu, 1986;Pearl, 1986Pearl, , 1987aPeng & Reggia, 1987;Weiss, Kulikowski, & Amarel, 1978) and logical approaches (Cox & Pietrzykowski, 1986;Fusaoka & Takahashi, 1986;Hagers & Hansson, 1983;Pearl, 1987b;Shoham, 1988). In our view, inductive nature of causation renders these perspectives quite limited as the sole basis for computational models.…”

Section: Bozsahin and Findlermentioning

confidence: 99%

Memory-based hypothesis formation: Heuristic learning of commonsense causal relations from text

Bozsahin

Findler

1992

Cognitive Science

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Section: Bozsahin and Findlermentioning

confidence: 99%

Memory‐Based Hypothesis Formation: Heuristic Learning of Commonsense Causal Relations from Text

Bozsahin

Findler

1992

Cognitive Science

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We present a memory‐based approach to learning commonsense causal relations from episodic text. The method relies on dynamic memory that consists of events, event schemata, episodes, causal heuristics, and causal hypotheses. The learning algorithms are based on applying causal heuristics to precedents of new information. The heuristics are derived from principles of causation, and, to a limited extent, from domain‐related causal reasoning. Learning is defined as finding—and later augmenting—inter‐episodal and intea‐episodal causal connections. The learning algorithms enable inductive generalization of causal associations into AND/OR graphs. The methodology has been implemented and tested in the program NEXUS.

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On a mechanical reasoning about causal relations

Cited by 5 publications

References 3 publications

Contributions to a Computer-Based Theory of Strategies

Contributions to a Computer-Based Theory of Strategies

Memory-based hypothesis formation: Heuristic learning of commonsense causal relations from text

Memory‐Based Hypothesis Formation: Heuristic Learning of Commonsense Causal Relations from Text

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