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Collective decision making has been a longstanding topic of study within swarm intelligence. The aim of this research area is to explain how groups of natural intelligent agents make decisions together, as well as to construct decision-making strategies that enable groups of artificial intelligent agents to come to a decision. The problems being investigated usually require the agents to form a collective decision using only their individual information and local interaction with their peers. There are two categories of problems that are primarily investigated within collective decision making, consensus achievement and task allocation. In the former category, agents need to form a singular opinion, while in the latter category, agents need to be allocated to different tasks.
In our research, we address the problem of collective perception, which is a discrete consensus achievement problem. We develop novel algorithms to deal with this problem