We present a method for context-dependent and incremental intention recognition by means of incrementally constructing a Bayesian Network (BN) model as more actions are observed. It is achieved with the support of a knowledge base of readily maintained and constructed fragments of BNs. The simple structure of the fragments enables to easily and efficiently acquire the knowledge base, either from domain experts or automatically from a plan corpus. We exhibit experimental results improvement for the Linux Plan corpus. For additional experimentation, new plan corpora for the iterated Prisoner's Dilemma are created. We show that taking into account contextual information considerably increases intention recognition performance.
|Number of pages||9|
|Journal||CEUR Workshop Proceedings|
|Publication status||Published - 1 Dec 2011|
|Event||8th Bayesian Modeling ApplicationsWorkshop, BMAW 2011 - Barcelona, Spain|
Duration: 14 Jul 2011 → 14 Jul 2011