TY - JOUR
T1 - Maintenance goals of agents in a dynamic environment
T2 - Formulation and policy construction
AU - Baral, Chitta
AU - Eiter, Thomas
AU - Bjäreland, Marcus
AU - Nakamura, Mutsumi
N1 - Funding Information:
This work was partially supported by FWF (Austrian Science Fund) projects P-16536-N04 and Z29-N04, the European Commission under grant IST 2001-37004 WASP, the NSF (National Science Foundation of USA) grant numbers 0070463, and 0412000, NASA grant number NCC2-1232, and contracts from ARDA and DTO. We would like to acknowledge W. Cushing for his feedback on an earlier draft and S. Gupta and M. Gouda for their clarifications on self-stabilization. Furthermore, we acknowledge comments by J. Rintanen on the ICAPS’04 paper and are grateful for his pointers to related work. We owe special thanks to F. Kabanza for helping us understand his co-authored papers [10,41] and making observations regarding how the algorithms in those papers could be modified to make them more efficient for specific goal specifications. We furthermore appreciate the constructive comments of the reviewers to improve the presentation, the suggestion of an LTL formulation in Section 4.1 and the suggestion of a linear time algorithm for generic maintainability. Finally, we are indebted to J. Zhao for implementing some of the algorithms and running the experiments, and appreciate the support of the DLV team.
PY - 2008/8
Y1 - 2008/8
N2 - The notion of maintenance often appears in the AI literature in the context of agent behavior and planning. In this paper, we argue that earlier characterizations of the notion of maintenance are not intuitive to characterize the maintenance behavior of certain agents in a dynamic environment. We propose a different characterization of maintenance and distinguish it from earlier notions such as stabilizability. Our notion of maintenance is more sensitive to a good-natured agent which struggles with an "adversary" environment, which hinders her by unforeseeable events to reach her goals (not in principle, but in case). It has a parameter k, referring to the length of non-interference (from exogenous events) needed to maintain a goal; we refer to this notion as k-maintainability. We demonstrate the notion on examples, and address the important but non-trivial issue of efficient construction of maintainability control functions. We present an algorithm which in polynomial time constructs a k-maintainable control function, if one exists, or tells that no such control is possible. Our algorithm is based on SAT Solving, and employs a suitable formulation of the existence of k-maintainable control in a fragment of SAT which is tractable. For small k (bounded by a constant), our algorithm is linear time. We then give a logic programming implementation of our algorithm and use it to give a standard procedural algorithm, and analyze the complexity of constructing k-maintainable controls, under different assumptions such as k = 1, and states described by variables. On the one hand, our work provides new concepts and algorithms for maintenance in dynamic environment, and on the other hand, a very fruitful application of computational logic tools. We compare our work with earlier works on control synthesis from temporal logic specification and relate our work to Dijkstra's notion of self-stabilization and related notions in distributed computing.
AB - The notion of maintenance often appears in the AI literature in the context of agent behavior and planning. In this paper, we argue that earlier characterizations of the notion of maintenance are not intuitive to characterize the maintenance behavior of certain agents in a dynamic environment. We propose a different characterization of maintenance and distinguish it from earlier notions such as stabilizability. Our notion of maintenance is more sensitive to a good-natured agent which struggles with an "adversary" environment, which hinders her by unforeseeable events to reach her goals (not in principle, but in case). It has a parameter k, referring to the length of non-interference (from exogenous events) needed to maintain a goal; we refer to this notion as k-maintainability. We demonstrate the notion on examples, and address the important but non-trivial issue of efficient construction of maintainability control functions. We present an algorithm which in polynomial time constructs a k-maintainable control function, if one exists, or tells that no such control is possible. Our algorithm is based on SAT Solving, and employs a suitable formulation of the existence of k-maintainable control in a fragment of SAT which is tractable. For small k (bounded by a constant), our algorithm is linear time. We then give a logic programming implementation of our algorithm and use it to give a standard procedural algorithm, and analyze the complexity of constructing k-maintainable controls, under different assumptions such as k = 1, and states described by variables. On the one hand, our work provides new concepts and algorithms for maintenance in dynamic environment, and on the other hand, a very fruitful application of computational logic tools. We compare our work with earlier works on control synthesis from temporal logic specification and relate our work to Dijkstra's notion of self-stabilization and related notions in distributed computing.
KW - Agent control
KW - Answer set programming
KW - Computational complexity of agent design
KW - Discrete event dynamic systems
KW - Horn theories
KW - Maintenance goals
KW - SAT solving
KW - Self-stabilization
KW - k-maintainability
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UR - http://www.scopus.com/inward/citedby.url?scp=46549088711&partnerID=8YFLogxK
U2 - 10.1016/j.artint.2008.03.005
DO - 10.1016/j.artint.2008.03.005
M3 - Article
AN - SCOPUS:46549088711
SN - 0004-3702
VL - 172
SP - 1429
EP - 1469
JO - Artificial Intelligence
JF - Artificial Intelligence
IS - 12-13
ER -