Compact Belief State Representation for Task Planning
| Autor: | Lorenzo Natale, Evgenii Safronov, Michele Colledanchise |
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| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
0209 industrial biotechnology Theoretical computer science Computer Science - Artificial Intelligence Binary decision diagram Computer science Probabilistic logic 02 engineering and technology State (functional analysis) Computer Science::Artificial Intelligence Cartesian product Directed acyclic graph Measure (mathematics) Task (project management) symbols.namesake Artificial Intelligence (cs.AI) 020901 industrial engineering & automation 0202 electrical engineering electronic engineering information engineering symbols 020201 artificial intelligence & image processing Representation (mathematics) |
| Zdroj: | CASE |
| DOI: | 10.1109/case48305.2020.9216994 |
| Popis: | Task planning in a probabilistic belief state domains allows generating complex and robust execution policies in those domains affected by state uncertainty. The performance of a task planner relies on the belief state representation. However, current belief state representation becomes easily intractable as the number of variables and execution time grows. To address this problem, we developed a novel belief state representation based on cartesian product and union operations over belief substates. These two operations and single variable assignment nodes form And-Or directed acyclic graph of Belief State (AOBS). We show how to apply actions with probabilistic outcomes and measure the probability of conditions holding over belief state. We evaluated AOBS performance in simulated forward state space exploration. We compared the size of AOBS with the size of Binary Decision Diagrams (BDD) that were previously used to represent belief state. We show that AOBS representation is not only much more compact than a full belief state but it also scales better than BDD for most of the cases. Accepted to CASE 2020 16th IEEE International Conference on Automation Science and Engineering |
| Databáze: | OpenAIRE |
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