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cs.AI2021
Computing Policies That Account For The Effects Of Human Agent Uncertainty During Execution In Markov Decision Processes
Sriram Gopalakrishnan, Mudit Verma, Subbarao Kambhampati
When humans are given a policy to execute, there can be policy execution errors and deviations in policy if there is uncertainty in identifying a state. This can happen due to the…
cs.AI2021
Integrating Planning, Execution and Monitoring in the presence of Open World Novelties: Case Study of an Open World Monopoly Solver
Sriram Gopalakrishnan, Utkarsh Soni, Tung Thai +3
The game of monopoly is an adversarial multi-agent domain where there is no fixed goal other than to be the last player solvent, There are useful subgoals like monopolizing sets of…