Multi-agent systems handle incomplete information by employing mechanisms that allow agents to make decisions based on the available data while considering the uncertainty stemming from missing or unknown information. These systems often utilize strategies like information sharing, belief management, and negotiation protocols to improve their overall understanding and actions. By designing agents to perform reasoning under uncertainty, the system can continue functioning effectively, even when not all information is present.
One common approach to managing incomplete information is through communication between agents. Agents can share what they know about their environment or their goals, helping to fill gaps in information. For instance, in a robotic swarm performing a search-and-rescue operation, individual robots might not be aware of their teammates' locations or statuses. By periodically broadcasting their position and any findings, the agents can collectively build a comprehensive picture of the search area, allowing them to coordinate better and respond to the situation more effectively.
Another important method is the use of belief frameworks, where each agent maintains a set of beliefs about the world, which includes known information and probabilities regarding unknown factors. For example, in a trading agent system, each agent might have incomplete knowledge about market conditions or the strategies of other traders. Agents can model their beliefs about these unknowns and update them based on the actions observed from others, adjusting their strategies to align with the perceived environment. By combining communication and belief management strategies, multi-agent systems can work collaboratively to mitigate incomplete information and make informed decisions that lead to better outcomes.