Abstract
The increased prevalence of agents raises numerous practical considerations. This paper addresses three of these - adaptability to unforeseen conditions, behavioral assurance, and timeliness of agent responses. Although these requirements appear contradictory, this paper introduces a paradigm in which all three are simultaneously satisfied. Agent strategies are initially verified. Then they are adapted by learning and formally reverified for behavioral assurance. This paper focuses on improving the time efficiency of reverification after learning. A priori proofs are presented that certain learning operators are guaranteed to preserve important classes of properties. In this case, efficiency is maximal because no reverification is needed. For those learning operators with negative a priori results, we present incremental algorithms that can substantially improve the efficiency of reverification.
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Gordon, D.F. (2001). APT Agents: Agents That Are Adaptive Predictable and Timely. In: Rash, J.L., Truszkowski, W., Hinchey, M.G., Rouff, C.A., Gordon, D. (eds) Formal Approaches to Agent-Based Systems. FAABS 2000. Lecture Notes in Computer Science(), vol 1871. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45484-5_22
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DOI: https://doi.org/10.1007/3-540-45484-5_22
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