Name
Title | ||
|---|---|---|
Constraint Satisfaction Propagation: Non-stationary Policy Synthesis for Temporal Logic Planning. |
Abstract | ||
|---|---|---|
Problems arise when using reward functions to capture dependencies between sequential time-constrained goal states because the state-space must be prohibitively expanded to accommodate a history of successfully achieved sub-goals. Policies and value functions derived with stationarity assumptions are not readily decomposable, leading to a tension between reward maximization and task generalization. We demonstrate a logic-compatible approach using model-based knowledge of environment dynamics and deadline information to directly infer non-stationary policies composed of reusable stationary policies. The policies are constructed to maximize the probability of satisfying time-sensitive goals while respecting time-varying obstacles. Our approach explicitly maintains two different spaces, a high-level logical task specification where the task-variables are grounded onto the low-level state-space of a Markov decision process. Computing satisfiability at the task-level is made possible by a Bellman-like equation which operates on a tensor that links the temporal relationship between the two spaces; the equation solves for a value function that can be explicitly interpreted as the probability of sub-goal satisfaction under the synthesized non-stationary policy, an approach we term Constraint Satisfaction Propagation (CSP). |
Year | Venue | DocType |
|---|---|---|
2019 | arXiv: Artificial Intelligence | Journal |
Volume | Citations | PageRank |
abs/1901.10405 | 0 | 0.34 |
References | Authors | |
19 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Thomas J. Ringstrom | 1 | 0 | 0.34 |
| Paul R. Schrater | 2 | 141 | 22.71 |