Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In partially observable worlds with many agents, nested beliefs are formed when agents simultaneously reason about the unknown state of the world and the beliefs of the other agents. The multi-agent filtering problem is to efficiently represent and update these beliefs through time as the agents act in the world. ,  formally define an infinite sequence of nested beliefs about the state of the world at the current time t, and present a filtering algorithm that maintains a finite representation which can be used to generate these beliefs. In some cases, this representation can be updated exactly in constant time;  also present a simple approximation scheme to compact beliefs if they become too complex. In experiments,  demonstrate efficient filtering in a range of multi-agent domains.
['Luke S. Zettlemoyer', 'Brian Milch', 'Leslie Pack Kaelbling']
I am writing a research paper on a idea Multi-Agent Filtering with Infinitely Nested Beliefs
[{"from": "human", "value": "I am writing a research paper on a idea Multi-Agent Filtering with Infinitely Nested Beliefs"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: In partially observable worlds with many agents, nested beliefs are formed when agents simultaneously reason about the unknown state of the world and the beliefs of the other agents. The multi-agent filtering problem is to efficiently represent and update these beliefs through time as the agents act in the world. ,  formally define an infinite sequence of nested beliefs about the state of the world at the current time t, and present a filtering algorithm that maintains a finite representation which can be used to generate these beliefs. In some cases, this representation can be updated exactly in constant time;  also present a simple approximation scheme to compact beliefs if they become too complex. In experiments,  demonstrate efficient filtering in a range of multi-agent domains."}]