Comparing Answer Set Programming and Hierarchical Knowledge Bases Regarding Comprehensibility and Reasoning Efficiency in the Context of Agents
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چکیده
In this paper, answer set programming and hierarchical knowledge bases are compared as knowledge representation paradigms for representing agent behavior. The comparison is based on two evaluation criteria: (1) comprehensibility (i. e., how easily the represented agent behavior can be comprehended by humans) and (2) reasoning efficiency (i. e., which of the two paradigms allows agents for more efficient reasoning about their next actions). It is shown that hierarchical knowledge bases seem to be the more comprehensible and more efficient approach for implementing agent behavior.
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تاریخ انتشار 2017