Every agent (combat unit) must plan and execute its own combat action series according to the instructions given by higher authority and its actual situation. Every combat action has a time interval, in order to guarantee the action's coordination within an agent or among some agents, every agent should analyze and compute the temporal relations between the actions that will be executed by its executors. Because there exist possible logical restricts and spacial, temporal or resource's conflicts between the actions, it's very important to correctly recognize temporal dependent relations between the actions, represent the preconditions of an action and design its executing action series for completion of combat mission. We discuss and analyze various dependent relations and put forward using Interval Algebra network and relation matrices to represent and compute the temporal relations.
In the military multi-agent system every agent needs to analyze the dependent and temporal relations among the tasks or combat behaviors for working-out its plans and getting the correct behavior sequences, it could guarantee good coordination, avoid unexpected damnification and guard against bungling the change of winning a battle due to the possible incorrect scheduling and conflicts. In this paper IA and PA network based computation of coordinating combat behaviors is put forward, and emphasize particularly on using 5x5 matrix to represent and compute the temporal binary relation (between two interval-events, two point-events or between one interval-event and one point-event), this matrix method makes the coordination computing convenience than before.
In the multi-node distributed decision system under some conditions there are a few or none permitted information exchange between the nodes, this makes the information fusion and final decision difficult. If one node looks as an agent, it has some other node's historical experiences or knowledge for resolving problems and stored in additional case bases, it can uses case based reasoning (CBR) and transposition reasoning to obtain the possible viewpoints or decisions of those nodes and then makes information fusion by itself, so may reduce the subjectivism which is weakness of pure transposition reasoning.