Ideomotor theory claims that perceptual representations of action-effects are functionally involved in the planning of actions. Strong evidence for this claim comes from a phenomenon called motorvisual priming. Motorvisual priming refers to the finding that action planning directly affects perception, and that the effects are selective for stimuli that share features with the planned action. Mo...

BACKGROUND Large discrepancies between people's intention to eat a healthy diet and actual dietary behavior indicate that motivation is not a sufficient instigator for healthy behavior. Research efforts to decrease this 'intention - behavior gap' have centered on aspects of self-regulation, most importantly self-regulatory planning. Most studies on the impact of self-regulatory planning in heal...

This paper proposes a novel method to schedule parallel execution of planning and action of a mobile robot. The method considers the following two types of parallelism. (1) acting while planning: if a partial planning result can be used to determine feasible actions, such actions can be executed while the planning process is still going. (2) planning while acting: if the result of the current a...

Résumé : This paper addresses a relational reinforcement learning-like problem with general action-model relational learning and planning techniques. We propose an integrated system for both action model learning and action selection in the context of adaptive behavior of autonomous agents. Learning is incremental. It operates with relational representations and produces disjunctions of 1st ord...

Previous findings suggest that planning an action "backward-primes" perceptual dimension related to this action: planning a grasp facilitates the processing of visual size information, while planning a reach facilitates the processing of location information. Here we show that dimensional priming of perception through action occurs even in the absence of active action planning. Subjects watched...

This paper reports on experiments where techniques of supervised machine learning are applied to the problem of planning. The input to the learning algorithm is composed of a description of a planning domain, planning problems in this domain, and solutions for them. The output is an eecient algorithm | a strategy | for solving problems in that domain. We test the strategy on an independent set ...

SIADEX is an integrated framework to support decision making during crisis episodes by providing realistic temporally annotated plans of action. The main component of SIADEX is a forward state-based HTN temporal planner.

patients and methods the current quasi-experimental study was conducted on 96 eligible females, who were allocated either to the case or the control group and were selected from homes in the border of zahedan city (southeast of iran) during 2010. convenience sampling by door-to-door visits was used for finding eligible cases. a ttm-based self-administrated family planning questionnaire was used...

Two main challenges of robot action planning in real domains are uncertain action effects and dynamic environments. In this paper, an instance-based action model is learned empirically by robots trying actions in the environment. Modeling the action planning problem as a Markov decision process, the action model is used to build the transition function. In static environments, standard value it...