Accurately determining intermediate and terminal plan states using bayesian goal recognition

Pattison, David and Long, Derek (2011) Accurately determining intermediate and terminal plan states using bayesian goal recognition. In: GAPRec 2011. Proceedings of the First Workshop on Goal, Activity and Plan Recognition. ICAPS, pp. 32-37.

[img]
Preview
PDF (paper)
paper.pdf
Final Published Version

Download (709kB)| Preview

    Abstract

    Goal Recognition concerns the problem of determining an agent's final goal, deduced from the plan they are currently executing (and subsequently being observed). The set of possible goals or plans to be considered are commonly stored in a library, which is then used to propose possible candidate goals for the agent's behaviour. Previously, we presented AUTOGRAPH - a system which removed the need for a goal or plan library, thus making any problem solvable without the need to construct such a structure. In this paper, we discuss IGRAPH, which improves upon its predecessor by utilising Bayesian inference to determine both terminal and intermediate goals/states which the agent being observed is likely to pass through.