TY - GEN
T1 - Reactive maintenance policies over equalized states in dynamic environments
AU - Saribatur, Zeynep G.
AU - Baral, Chitta
AU - Eiter, Thomas
N1 - Funding Information:
This work has been supported by Austrian Science Fund (FWF) project W1255-N23, and Zeynep G. Saribatur’s visit to ASU was supported by the Austrian Marshall Plan Foundation.
Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - We address the problem of representing and verifying the behavior of an agent following a policy in dynamic environments. Our focus is on policies that yield sequences of actions, according to the present knowledge in the state, with the aim of reaching some main goal. We distinguish certain cases where the dynamic nature of the environment may require the agent to stop and revise its next actions. We employ the notion of maintenance to check whether a given policy can maintain the conditions of the main goal, given a respite from environment actions. Furthermore, we apply state clustering to mitigate the large state spaces caused by having irrelevant information in the states, and under some conditions this clustering might change the worst-case complexity. By preserving the behavior of the policy, it helps in checking for maintenance with a guarantee that the result also holds in the original system.
AB - We address the problem of representing and verifying the behavior of an agent following a policy in dynamic environments. Our focus is on policies that yield sequences of actions, according to the present knowledge in the state, with the aim of reaching some main goal. We distinguish certain cases where the dynamic nature of the environment may require the agent to stop and revise its next actions. We employ the notion of maintenance to check whether a given policy can maintain the conditions of the main goal, given a respite from environment actions. Furthermore, we apply state clustering to mitigate the large state spaces caused by having irrelevant information in the states, and under some conditions this clustering might change the worst-case complexity. By preserving the behavior of the policy, it helps in checking for maintenance with a guarantee that the result also holds in the original system.
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U2 - 10.1007/978-3-319-65340-2_58
DO - 10.1007/978-3-319-65340-2_58
M3 - Conference contribution
AN - SCOPUS:85028965353
SN - 9783319653396
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 709
EP - 723
BT - Progress in Artificial Intelligence - 18th EPIA Conference on Artificial Intelligence, EPIA 2017, Proceedings
A2 - Vale, Zita
A2 - Oliveira, Eugenio
A2 - Gama, Joao
A2 - Lopes Cardoso, Henrique
PB - Springer Verlag
T2 - 18th EPIA Conference on Artificial Intelligence, EPIA 2017
Y2 - 5 September 2017 through 8 September 2017
ER -