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The game of monopoly is an adversarial multi-agent domain where there is nofixed goal other than to be the last player solvent, There are useful subgoalslike monopolizing sets of properties, and developing them. There is also a lotof randomness from dice rolls, card-draws, and adversaries' strategies. Thisunpredictability is made worse when unknown novelties are added duringgameplay. Given these challenges, Monopoly was one of the test beds chosen forthe DARPA-SAILON program which aims to create agents that can detect andaccommodate novelties. To handle the game complexities, we developed an agentthat eschews complete plans, and adapts it's policy online as the game evolves.In the most recent independent evaluation in the SAILON program, our agent wasthe best performing agent on most measures. We herein present our approach andresults.

Integrating Planning, Execution and Monitoring in the presence of Open World Novelties: Case Study of an Open World Monopoly Solver