Summary of Heuristics For Partially Observable Stochastic Contingent Planning, by Guy Shani
Heuristics for Partially Observable Stochastic Contingent Planning
by Guy Shani
First submitted to arxiv on: 8 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed research develops a new heuristic function for solving goal-based Partially Observable Markov Decision Processes (POMDPs). The RTDP-BEL algorithm is used to solve these POMDPs, which involves running forward trajectories from the initial belief to the goal. By leveraging structured domain models and computing plans in a relaxed space, this heuristic function takes into account both the value of information and stochastic effects. Experimental results show that while it may be slower to compute, this heuristic requires significantly fewer trajectories before convergence, ultimately speeding up RTDP-BEL, particularly in problems where significant information gathering is necessary. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps artificial intelligence complete tasks more efficiently by developing a new way to solve complex decision-making problems called goal-based POMDPs. It creates a guide that uses structured knowledge about the problem and takes into account how much information is needed to make good decisions. This guide makes it possible for computers to find solutions faster, especially when they need to gather a lot of information. |
