Optimal Policy Generation for Partially Satisfiable Co-Safe LTL Specifications

B. Lacerda, D. Parker, N. Hawes

Research output: Chapter in Book/Report/Conference proceedingConference contribution

24 Citations (Scopus)
9 Downloads (Pure)

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 languageEnglish
Title of host publicationProc. 24th International Joint Conference on Artificial Intelligence (IJCAI'15)
EditorsQiang Yang, Michael Wooldridge
PublisherAssociation for the Advancement of Artificial Intelligence
Pages1587-1593
ISBN (Print)9781577357384
Publication statusPublished - 20 Jul 2015
EventInternational Joint Conference on Artificial Intelligence, 24th (ICJAI 2015) - Buenos Aires, Argentina
Duration: 25 Jul 201531 Jul 2015

Conference

ConferenceInternational Joint Conference on Artificial Intelligence, 24th (ICJAI 2015)
Country/TerritoryArgentina
Period25/07/1531/07/15

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