Name
Abstract | ||
|---|---|---|
We present a method to calculate cost-optimal policies for task specifications in co-safe linear temporal logic 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. |
Year | Venue | Field |
|---|---|---|
2015 | IJCAI | Mathematical optimization,Computer science,Markov decision process,Linear temporal logic,Real-time computing,Robot,Probabilistic model checking |
DocType | Citations | PageRank |
Conference | 4 | 0.44 |
References | Authors | |
17 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Bruno Lacerda | 1 | 85 | 12.96 |
| David Parker 0001 | 2 | 19 | 2.09 |
| Nick Hawes | 3 | 321 | 34.18 |