| 研究実績の概要 |
In FY2019, we focused on (1) computational issues in Belief Revision Games (BRGs), and (2) identifying what (initially unknown) information was announced to a set of agents by observing their prior and posterior (changed) beliefs.
(1) We addressed the computational issues in BRGs and focused on the case where the revision policies of the agents are based on a well-known majority-based merging operator. In particular, we showed how some evolution patterns in the agents' belief sequences can be identified independently of the propositional language used by the agents to express their beliefs, allowing an exhaustive search of all possible belief cycle patterns.
(2) We considered the problem of identifying the change formula in a belief revision scenario: given that an unknown announcement led a set of agents to revise their beliefs and given the prior beliefs and the revised beliefs of the agents, what can be said about the announcement? We presented some sufficient conditions for its characterization, identified their computational complexity, and reported the results of some experiments about it.
|
