Motivation-based direction of planning attention in agents with goal autonomy

The action of an agent with goal autonomy will be driven by goals generated with reference to its own beliefs and desires. This ability is essential for agents that are required to act in their own interests in a domain that is not entirely predictable. At any time, the situation may warrant the generation of new goals. However, it is not always the case that changes in the domain that lead to the generation of a goal are detected immediately before the goal should be pursued. Action may not be appropriate for some time. Furthermore, an agent may be influenced by goals that tend to recur periodically, or at particular times of the day or week for example. Such goals serve to motivate an agent towards interacting with other agents or processes with certain types of predictable behaviour patterns. This thesis provides a model of a goal autonomous agent that may generate goals in response to unexpected changes in its domain or cyclically through automatic processes. An important effect of goal autonomy is that the agent exhibiting this capability will have a varying, potentially unlimited, but certainly unpredictable number of goals. Goals that hold planning attention consume resources, and real agents are resource bounded. Hence, there is a limit to the number of goals that can hold planning attention before bookkeeping and search operations become the primary mode of activity; i.e. before cognitive overload. In this thesis, an heuristic mechanism is proposed for the directing and limiting of planning attention in agents with goal autonomy. These "alarm processing" mechanisms serve to focus the attention of an agent on a limited number of the most salient goals, and thereby avoid unnecessary reasoning and prevent cognitive overload. In this way, a resource-bounded agent can employ modern planning and reasoning methods effectively

Using Abstraction and Nondeterminism to Plan Reaction Loops

ion and Nondeterminism to Plan Reaction Loops David J. Musliner Institute for Advanced Computer Studies The University of Maryland College Park, Maryland 20742 [email protected] Abstract By looping over a set of behaviors, reactive systems use repetition and feedback to deal with errors and environmental uncertainty. Their robust, fault-tolerant performance makes reactive systems desirable for executing plans. However, most planning systems cannot reason about the loops that characterize reactive systems. In this paper, we show how the structured application of abstraction and nondeterminism can map complex planning problems requiring loop plans into a simpler representation amenable to standard planning technologies. In the process, we illustrate key recipes for automatically building predictable reactive systems that are guaranteed to achieve their goals. Introduction The uncertainty inherent in real-world domains has proven problematic for traditional AI planning technolo..