We present SET-PR, a novel case-based plan recognition algorithm that is tolerant to missing and misclassified actions in its input action sequences. SET-PR uses a novel representation called action sequence graphs to represent stored plans in its plan library and a similarity metric that uses a combination of graph degree sequences and object similarity to retrieve relevant plans from its libr...

This paper describes a plan parsing method that can handle the effects and preconditions of actions and that parses plans in a manner dependent on dialogue state changes, especially on the mental state changes of dialogue participants caused by utterances. This method is based on active chart parsing and uses augmented edge structures to keep state information locally and time map management to...

When the real outcomes of actions are not completely represented, a planner may not be able to construct a valid plan even if there exists one. This research focuses on generic domain update and reasoning methods to construct alternative plans against real-time execution failures which are detected either during runtime or earlier by a plan simulation process. Based on the updated domain repres...

When we plan sequences of actions, we must hold some elements of the sequence in working memory (WM) while we execute others. Research shows that execution of an action can be delayed if it partly overlaps (vs. does not overlap) with another action plan maintained in WM (partial repetition cost). However, it is not known whether all features of the action maintained in WM interfere equally with...

Through the Global Strategy for the Prevention and Control of Noncommunicable Diseases and its 2008–2013 Action Plan, Member States signalled the pressing need for countries and the international community to take concrete and sustained action to reverse the NCD epidemic. Both the Global Strategy and the Action Plan were developed through the active engagement of Member States, and vigorously d...

In this paper we propose a novel computation model for the execution of abstract action chains. In this computation model a robot first learns situation-specific performance models of abstract actions. It then uses these models to automatically specialize the abstract actions for their execution in a given action chain. This specialization results in refined chains that are optimized for perfor...

In this application oriented paper we develop information fusion explanation functions for simulation-based decision support for evaluation of military plans in expeditionary operations. The explanation function is based on a sensitivity analysis on the impact of different actions upon the success of the plan where we systematically vary the alternatives of each action of the plan, one action a...