Intentions in Means-End Planning (Dissertation Proposal)

This proposal discusses the use of the intentions of the actor in performing means-end reasoning. In doing so, it will show that preconditions and applicability conditions in existing systems are ill-defined and intrinsically encode situational information that prevents intentions from playing a role in the planning process. While the former problem can be fixed, the latter cannot. Therefore, I argue that preconditions should be eliminated from action representation. In their place, I suggest explicit representation of intention, situated reasoning about the results of action, and robust failure mechanisms. I then describe a system, the Intentional Planning System (ItPlanS), which embodies these ideas, compare ItPlanS to other systems, and propose future directions for this work

A Reconsideration of Preconditions

This paper is part of an attempt to introduce intentionality of the actor to planning decisions. As a first step in this process the usual representations for actions used by planning systems must be reevaluated. this paper argues for the elimination of preconditions and qualification conditions from action representation in favor of explicit representation of intention, situated reasoning about the results of the action and reactive failure mechanisms. The paper then describes a planning system that has explicit representation and use of intentions and uses action representation that do not have preconditions

Object Action Complexes as an Interface for Planning and Robot Control

Abstract — Much prior work in integrating high-level artificial intelligence planning technology with low-level robotic control has foundered on the significant representational differences between these two areas of research. We discuss a proposed solution to this representational discontinuity in the form of object-action complexes (OACs). The pairing of actions and objects in a single interface representation captures the needs of both reasoning levels, and will enable machine learning of high-level action representations from low-level control representations. I. Introduction and Background The different representations that are effective for continuous control of robotic systems and the discrete symbolic AI presents a significant challenge for integrating AI planning research and robotics. These areas of research should be abl

The Meaning of Action:a review on action recognition and mapping

In this paper, we analyze the different approaches taken to date within the computer vision, robotics and artificial intelligence communities for the representation, recognition, synthesis and understanding of action. We deal with action at different levels of complexity and provide the reader with the necessary related literature references. We put the literature references further into context and outline a possible interpretation of action by taking into account the different aspects of action recognition, action synthesis and task-level planning

A New Model of Plan Recognition

We present a new abductive, probabilistic theory of plan recognition. This model differs from previous plan recognition theories in being centered around a model of plan execution: most previous methods have been based on plans as formal objects or on rules describing the recognition process. We show that our new model accounts for phenomena omitted from most previous plan recognition theories: notably the cumulative effect of a sequence of observations of partially-ordered, interleaved plans and the effect of context on plan adoption. The model also supports inferences about the evolution of plan execution in situations where another agent intervenes in plan execution. This facility provides support for using plan recognition to build systems that will intelligently assist a user.Comment: Appears in Proceedings of the Fifteenth Conference on Uncertainty in Artificial Intelligence (UAI1999