Abstract
Apriori Stochastic Dependency Detection (ASDD) is an algorithm for fast induction of stochastic logic rules from a database of observations made by an agent situated in an environment. ASDD is based on features of the Apriori algorithm for mining association rules in large databases of sales transactions [1] and the MSDD algorithm for discovering stochastic dependencies in multiple streams of data [15]. Once these rules have been acquired the Precedence algorithm assigns operator precedence when two or more rules matching the input data are applicable to the same output variable. These algorithms currently learn propositional rules, with future extensions aimed towards learning first-order models. We show that stochastic rules produced by this algorithm are capable of reproducing an accurate world model in a simple predator-prey environment.
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Child, C., Stathis, K. (2004). The Apriori Stochastic Dependency Detection (ASDD) Algorithm for Learning Stochastic Logic Rules. In: Dix, J., Leite, J. (eds) Computational Logic in Multi-Agent Systems. CLIMA 2004. Lecture Notes in Computer Science(), vol 3259. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30200-1_13
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DOI: https://doi.org/10.1007/978-3-540-30200-1_13
Publisher Name: Springer, Berlin, Heidelberg
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