Reasoning about goal-plan trees in autonomous agents : development of petri net and constraint-based approaches with resulting performance comparisons

Multi-agent systems and autonomous agents are becoming increasingly important in current computing technology. In many applications, the agents are often asked to achieve multiple goals individually or within teams where the distribution of these goals may be negotiated among the agents. It is expected that agents should be capable of working towards achieving all its currently adopted goals concurrently. However, in doing so, the goals can interact both constructively and destructively with each other, so a rational agent must be able to reason about these interactions and any other constraints that may be imposed on them, such as the limited availability of resources that could affect their ability to achieve all adopted goals when pursuing them concurrently. Currently, agent development languages require the developer to manually identify and handle these circumstances. In this thesis, we develop two approaches for reasoning about the interactions between the goals of an individual agent. The first of these employs Petri nets to represent and reason about the goals, while the second uses constraint satisfaction techniques to find efficient ways of achieving the goals. Three types of reasoning are incorporated into these models: reasoning about consumable resources where the availability of the resources is limited; the constructive interaction of goals whereby a single plan can be used to achieve multiple goals; and the interleaving of steps for achieving different goals that could cause one or more goals to fail. Experimental evaluation of the two approaches under various different circumstances highlights the benefits of the reasoning developed here whilst also identifying areas where one approach provides better results than the other. This can then be applied to suggest the underlying technique used to implement the reasoning that the agent may want to employ based on the goals it has been assigned.