Hidden Markov Model to Optimize Coordination Relationship for Learning Behaviour

Abstract

School communities interact dynamically, much like the agents in a multi-agent system. For coordinated action, relationships between agents in a multi-agent system must be handled. One technique for persuading individuals to behave in a coordinated manner is to manage the role of agents in generating knowledge, attitudes, and practices. Managing these connections is difficult due to the large number of unknowns. Modeling can aid in the clarification of agent relationships. Coordination mechanisms can be modeled using Markov models. Agents can demonstrate and consider how their actions affect other agents in order to achieve desired behavior goals. This paper extends the state space of Partially Observable Markov Decision Processes (POMDPs) with an agent model to make them multi-agent friendly.