Abstract
Many real-world planning problems involve sub stantial amounts of domain-specific reasoning that is either awkward or inefficient to encode in a general purpose planner. Previous approaches for planning in such domains have either been largely domain specific or have compromised with shallow models of the domain-specific considerations. In this paper, we propose a hybrid planning architecture for such domains, which utilizes a set of specialists to complement both the overall expressiveness and the efficiency of a traditional hierarchical planner. Such an architecture promises to retain the flexibility and generality of classical planning framework while allowing deeper and more efficient domain-specific reasoning through specialists. The architecture, however, has several ramifications on the internal operations of the planner as well as its interactions with the specialists. First, continual interactions between the planner and the specialists necessitate an incremental, interactive, and least-commitment oriented approach to planning. Second, as the planner and the specialists in such a model may employ heterogeneous reasoning mechanisms and representations, a complete understanding of the operations of one by the other is not possible. This necessitates designing interfaces at the right level of abstraction, to efficiently mediate the interactions between them. In this paper, we investigate these issues with the help of our implementation of a hybrid planning architecture for a manufacturing planning domain.
| Original language | English |
|---|---|
| Pages (from-to) | 1503-1518 |
| Number of pages | 16 |
| Journal | IEEE Transactions on Systems, Man and Cybernetics |
| Volume | 23 |
| Issue number | 6 |
| DOIs | |
| Publication status | Published - 1993 |
Bibliographical note
Funding Information:Manuscript received March 22. 1992; revised February 17, 1993. This research was supported in part by the Office of Naval Research under contract N00014-88-K-0620 by the National Science Foundation under grant 1RI-9210997, and in part by ARPAIROME Laboratory Planning Initiative under grant F30602-93-(-0039. A preliminary version of this paper has been presented at the 9th National Conference on Artificial Intelligence, Anaheim, CA, July, 1991 [IS]. S. Kambhampati was with the Center for Design Research and Department of Computer Science, Stanford University, Stanford, CA 94305. He is now with the Department of Computer Science and Engineering, Arizona State University, Tempe, AZ 85287. M. S. Cutkosky and S. H. Lee are with the Center for Design Research, Stanford University, Stanford, CA 94305. J. M. Tenenbaum is with Enterprise Integration Technology, Palo Alto, CA 94301. He is also affiliated with the Department of Computer Science, Stanford University, Stanford, CA 94305. IEEE Log Number 9212933.
All Science Journal Classification (ASJC) codes
- Engineering(all)