Partially Observable Markov Decision Processes (POMDP) provide a standard framework for sequential decision making in stochastic environments. In this setting, an agent takes actio...
In this paper, we introduce an alternative approach to reasoning about action. The approach provides a solution to the frame and the ramification problem in a uniform manner. The ...
We consider the iterated belief change that occurs following an alternating sequence of actions and observations. At each instant, an agent has some beliefs about the action that ...
This paper extends the framework of partially observable Markov decision processes (POMDPs) to multi-agent settings by incorporating the notion of agent models into the state spac...
Abstract. One of the most difficult problems in multiagent systems involves representing knowledge and beliefs of agents in dynamic environments. New perceptions modify an agent’...