The theory and practice of intention reconsideration

One of the key problems in the design of belief-desire-intention (BDI) agents is that of finding an appropriate policy for intention reconsideration. Crudely, the idea is that at any given time, an agent will have a number of intentions, relating to states of affairs that the agent has committed to bring about. An agent chooses plans that are appropriate for bringing about these intentions; if a particular plan for a given intention fails, then the agent will typically replan, to find an alternative course of action for this intention. However, a rational agent’s intentions will not be static. From time-to-time, it makes sense for such an agent to reconsider its intentions, for example when the intention is doomed never to be realized, or else when the agent would simply profit from adopting another, more fruitful goal. This paper presents a detailed investigation of the properties of intention reconsideration. The work builds on the foundational work of Kinny and Georgeff, who investigated the properties of various intention reconsideration strategies in environments that were to varying degrees dynamic, i.e. subject to unanticipated change. The present paper broadly falls into two distinct parts. In the first part, the authors extend work of Kinny and Georgeff, by investigating the properties of intention reconsideration strategies in environments that are also to varying degrees (in)accessible and (non-)deterministic. They then investigate two different models of intention reconsideration. In the first model, intention reconsideration is modelled as a process of discrete deliberation scheduling: intention reconsideration is modelled as an action that may be performed by an agent, and so lends itself to analysis in terms of conventional decision theoretic models of optimal action. In the second, intention reconsideration is modelled as a partially observable Markov decision process (POMDP): solving the POMDP means finding an optimal intention reconsideration policy. J. Expt. Theor. Artif. Intell. Vol. 16, No. 4, October–December, 2004, 261–293 Journal of Experimental & Theoretical Artificial Intelligence ISSN 0952–813X print/ISSN 1362–3079 online # 2004 Taylor & Francis Ltd http://www.tandf.co.uk/journals DOI: 10.1080/09528130412331309277