Agent Behavior alignment: A mechanism to overcome problems in agent interactions during runtime

When two or more agents interacting, their behaviors are not necessarily matching. Automated ways to overcome conflicts in the behavior of agents can make the execution of interactions more reliable. Such an alignment mechanism will reduce the necessary human intervention. This paper shows how to describe a policy for alignment, which an agent can apply when its behavior is in conflict with other agents. An extension of Petri Nets is used to capture the intended interaction of an agent in a formal way. Furthermore, a mechanism based on machine learning is implemented, to enable an agent to choose an appropriate alignment policy with collected problem information. Human intervention can reinforce certain successful policies in a given context, and can also contribute by adding completely new policies. Experiments have been conducted to test the applicability of the alignment mechanism and the main results are presented here