Norman, Timothy James Forester; (1997) Motivation-based direction of planning attention in agents with goal autonomy. Doctoral thesis (Ph.D), UCL (University College London).

Preview

Text Motivation-based_direction_of_.pdf Download (15MB) | Preview

Abstract

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.

Download activity - last month

Export as JSONXMLCSV

Download activity - last 12 months

Export as JSONCSVXML

Downloads by country - last 12 months

Export as JSONXMLCSV

Archive Staff Only

View Item