Name
Abstract | ||
|---|---|---|
The increasedprev alence 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 andformally reverifiedfor behavioral assurance. This paper focuses on improving the time efficiency of reverification after learning. A priori proofs are presentedthat certain learning operators are guaranteedto 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. |
Year | DOI | Venue |
|---|---|---|
2000 | 10.1007/3-540-45484-5_22 | Lecture Notes in Computer Science |
Keywords | Field | DocType |
apt agents,agent strategy,incremental algorithm,behavioral assurance,andformally reverifiedfor behavioral assurance,time efficiency,presentedthat certain learning operator,increasedprev alence,important class,numerous practical consideration,agent response,satisfiability | Adaptability,Computer science,A priori and a posteriori,Risk analysis (engineering),Mathematical proof,Artificial intelligence,Operator (computer programming) | Conference |
ISBN | Citations | PageRank |
3-540-42716-3 | 6 | 0.59 |
References | Authors | |
16 | 1 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Diana F. Gordon | 1 | 502 | 70.20 |