Name
Abstract | ||
|---|---|---|
Reasoning plays a central role in intelligent systems that operate in complex situations that involve time constraints. In this paper, we present the Adaptive Logic Interpreter, a reasoning system that acquires a controlled inference strategy adapted to the scenario at hand, using a variation on relational reinforcement learning. Employing this inference mechanism in a reactive agent architecture lets the agent focus its reasoning on the most rewarding parts of its knowledge base and hence perform better under time and computational resource constraints. We present experiments that demonstrate the benefits of this approach to reasoning in reactive agents, then discuss related work and directions for future research. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11536314_2 | ILP |
Keywords | Field | DocType |
inference mechanism,present experiment,central role,controlled inference strategy,adaptive logic interpreter,reasoning system,reactive agent architecture,relational reinforcement learning,guiding inference,complex situation,reactive agent,time constraint,knowledge base,agent architecture | Intelligent agent,Intelligent decision support system,Computer science,Inference,Model-based reasoning,Agent architecture,Artificial intelligence,Opportunistic reasoning,Reasoning system,Reinforcement learning | Conference |
Volume | ISSN | ISBN |
3625 | 0302-9743 | 3-540-28177-0 |
Citations | PageRank | References |
8 | 0.58 | 12 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nima Asgharbeygi | 1 | 29 | 2.86 |
| Negin Nejati | 2 | 94 | 5.95 |
| Pat Langley | 3 | 6471 | 1307.64 |
| Sachiyo Arai | 4 | 35 | 5.79 |