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UMCP: a sound and complete procedure for hierarchical task-network planning

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TLDR
This paper presents a formal syntax and semantics for HTn planning and is able to define an algorithm for HTN planning and prove it sound and complete.
Abstract
One big obstacle to understanding the nature of hierarchical task network (HTN) planning has been the lack of a clear theoretical framework In particular, no one has yet presented a clear and concise HTN algorithm that is sound and complete In this paper, we present a formal syntax and semantics for HTN planning Based on this syntax and semantics, we are able to define an algorithm for HTN planning and prove it sound and complete

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Journal ArticleDOI

Learning and verifying safety constraints for planners in a knowledge-impoverished system

TL;DR: A Bayesian approach to learning flexible safety constraints and subsequently verifying whether plans satisfy these constraints, called the Safety Constraint Learner/Checker (SCLC), which is integrated within the Generalized Integrated Learning Architecture.

Hierarchical Task Network Planning Using SAT Techniques

TL;DR: The proposed T-REX planning framework outperforms a state-of-the-art classical SAT planner on various domains and demonstrates that HTN planning via SAT solving is a viable option and worthy of the attention of future research.
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Building maps from incomplete environment information: a cognitive approach based on the generation of expectations

TL;DR: A cognitive approach for mapping that relies heavily on the generation of assumptions/expectations for the missing observational information and the architecture for an explorer-agent that uses this approach to build maps and whose behaviour is guided by the emotions, drives and other motivations that it may “feel”.
Book ChapterDOI

Automated teleoperation of web-based devices using semantic web services

TL;DR: SWATS is presented which supports task-oriented automated teleoperation of Web-based devices and employs Semantic Web Services technology and AI planning technique to achieve operational automaticity.
References
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Principles of Artificial Intelligence

TL;DR: This classic introduction to artificial intelligence describes fundamental AI ideas that underlie applications such as natural language processing, automatic programming, robotics, machine vision, automatic theorem proving, and intelligent data retrieval.
Book

STRIPS: a new approach to the application of theorem proving to problem solving

TL;DR: In this article, the authors describe a problem solver called STRIPS that attempts to find a sequence of operators in a spcce of world models to transform a given initial world model into a model in which a given goal formula can be proven to be true.
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A Structure for Plans and Behavior

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Journal ArticleDOI

Planning for Conjunctive Goals

TL;DR: Theorems that suggest that efficient general purpose planning with more expressive action representations is impossible are presented, and ways to avoid this problem are suggested.
Proceedings Article

UCPOP: a sound, complete, partial order planner for ADL

TL;DR: It is proved ucpop is both sound and complete for this representation and a practical implementation that succeeds on all of Pednault's and McDermott's examples, including the infamous "Yale Stacking Problem".
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