Summary of Anticipating Oblivious Opponents in Stochastic Games, by Shadi Tasdighi Kalat and Sriram Sankaranarayanan and Ashutosh Trivedi
Anticipating Oblivious Opponents in Stochastic Games
by Shadi Tasdighi Kalat, Sriram Sankaranarayanan, Ashutosh Trivedi
First submitted to arxiv on: 18 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Systems and Control (eess.SY)
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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 approach systematically anticipates the actions and policies employed by oblivious environments in concurrent stochastic games while maximizing a reward function. The method synthesizes a finite information state machine whose alphabet ranges over the actions of the environment, mapping each state to a belief state about the policy used. A consistency notion ensures that the tracked belief states stay within a fixed distance of the precise belief state obtained with full history knowledge. The approach checks consistency and yields an MDP for computing optimal policies for maximizing a reward function defined over plays. Experimental evaluation on benchmark examples, including human activity data for tasks such as cataract surgery and furniture assembly, demonstrates successful policy anticipation and maximum reward attainment. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how to predict what will happen when we interact with an environment that doesn’t know what it’s doing. It uses a special kind of machine called an information state machine to figure out the environment’s actions and policies. The machine keeps track of its beliefs about the environment’s policy, making sure they’re consistent with the data it has. This approach can be used to make decisions based on rewards, like maximizing profit or minimizing risk. The paper shows that this method works well in different scenarios, including real-life tasks like surgery and furniture assembly. |
