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Computationally Effective Reasoning About Goal Interactions

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Abstract

It is important that intelligent agents are able to pursue multiple goals in parallel, in a rational manner. In this work we have described the careful empirical evaluation of the value of data structures and algorithms developed for reasoning about both positive and negative goal interactions. These mechanisms are incorporated into a commercial agent platform and then evaluated in comparison to the platform without these additions. We describe the data structures and algorithms developed, and the X-JACK system, which incorporates these into JACK, a state of the art agent development toolkit. There are three basic kinds of reasoning that are developed: reasoning about resource conflicts, reasoning to avoid negative interactions that can happen when steps of parallel goals are arbitrarily interleaved, and reasoning to take advantage of situations where a single step can help to achieve multiple goals. X-JACK is experimentally compared to JACK, under a range of situations designed to stress test the reasoning algorithms, as well as situations designed to be more similar to real applications. We found that the cost of the additional reasoning is small, even with large numbers of goal interactions to reason about. The benefit however is noticeable, and is statistically significant, even when the amount of goal interactions is relatively small.

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  1. School of Computer Science, RMIT University, Melbourne, Australia

    John Thangarajah & Lin Padgham

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  1. John Thangarajah
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  2. Lin Padgham
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Correspondence to John Thangarajah.

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Thangarajah, J., Padgham, L. Computationally Effective Reasoning About Goal Interactions. J Autom Reasoning 47, 17–56 (2011). https://doi.org/10.1007/s10817-010-9175-0

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  • DOI: https://doi.org/10.1007/s10817-010-9175-0

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