Context-dependent incremental decision making scrutinizing the intentions of others via Bayesian network model construction

Decision making about which are the scrutinized intentions of others, usually called intention reading or intention recognition, is an elementary basic decision making process required as a basis for other higher-level decision making, such as the intention-based decision making which we have set forth in previous work. We present herein a recognition method possessing several features desirable of an elementary process: (i) The method is context-dependent and incremental, enabling progressive construction of a three-layer Bayesian network model as more actions are observed, and in a context-situated manner that relies on a logic programming knowledge base concerning the context; (ii) The Bayesian network is structured from a specific knowledge base of readily specified and readily maintained Bayesian network fragments with simple structures, thereby enabling the efficient acquisition of that knowledge base (engineered either by domain experts or else automatically from a plan corpus); and, (iii) The method addresses the issue of intention change and abandonment, and can appropriately resolve the issue of the recognition of multiple intentions. The several aspects of the method have been experimentally evaluated in applications and achieving definite success, using the Linux plan corpus and the so-called IPD plan corpora, which are playing sequences generated by game playing strategies needing to be recognized, in the iterated Prisoner's Dilemma. One other application concerns variations of Elder Care in the context of Ambient Intelligence.