Optimal Policy Generation for Partially Satisfiable Co-Safe LTL Specifications
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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Optimal Policy Generation for Partially Satisfiable Co-Safe LTL Specifications. / Lacerda, B.; Parker, D.; Hawes, N.
Proc. 24th International Joint Conference on Artificial Intelligence (IJCAI'15). ed. / Qiang Yang; Michael Wooldridge. Association for the Advancement of Artificial Intelligence, 2015. p. 1587-1593.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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TY - GEN
T1 - Optimal Policy Generation for Partially Satisfiable Co-Safe LTL Specifications
AU - Lacerda, B.
AU - Parker, D.
AU - Hawes, N.
PY - 2015/7/20
Y1 - 2015/7/20
N2 - We present a method to calculate cost-optimal policies for co-safe linear temporal logic task specifications over a Markov decision process model of a stochastic system. Our key contribution is to address scenarios in which the task may not be achievable with probability one. We formalise a task progression metric and, using multi-objective probabilistic model checking, generate policies that are formally guaranteed to, in decreasing order of priority: maximise the probability of finishing the task; maximise progress towards completion, if this is not possible; and minimise the expected time or cost required. We illustrate and evaluate our approach in a robot task planning scenario, where the task is to visit a set of rooms that may be inaccessible during execution.
AB - We present a method to calculate cost-optimal policies for co-safe linear temporal logic task specifications over a Markov decision process model of a stochastic system. Our key contribution is to address scenarios in which the task may not be achievable with probability one. We formalise a task progression metric and, using multi-objective probabilistic model checking, generate policies that are formally guaranteed to, in decreasing order of priority: maximise the probability of finishing the task; maximise progress towards completion, if this is not possible; and minimise the expected time or cost required. We illustrate and evaluate our approach in a robot task planning scenario, where the task is to visit a set of rooms that may be inaccessible during execution.
M3 - Conference contribution
SN - 9781577357384
SP - 1587
EP - 1593
BT - Proc. 24th International Joint Conference on Artificial Intelligence (IJCAI'15)
A2 - Yang, Qiang
A2 - Wooldridge, Michael
PB - Association for the Advancement of Artificial Intelligence
T2 - International Joint Conference on Artificial Intelligence, 24th (ICJAI 2015)
Y2 - 25 July 2015 through 31 July 2015
ER -