Abstract
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.
Original language | English |
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Title of host publication | Proc. 24th International Joint Conference on Artificial Intelligence (IJCAI'15) |
Editors | Qiang Yang, Michael Wooldridge |
Publisher | Association for the Advancement of Artificial Intelligence |
Pages | 1587-1593 |
ISBN (Print) | 9781577357384 |
Publication status | Published - 20 Jul 2015 |
Event | International Joint Conference on Artificial Intelligence, 24th (ICJAI 2015) - Buenos Aires, Argentina Duration: 25 Jul 2015 → 31 Jul 2015 |
Conference
Conference | International Joint Conference on Artificial Intelligence, 24th (ICJAI 2015) |
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Country/Territory | Argentina |
Period | 25/07/15 → 31/07/15 |