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An Introduction to the Planning Domain Definition Language
Planning is the branch of Artificial Intelligence (AI) that seeks to automate reasoning about plans, most importantly the reasoning that goes into formulating a plan to achieve a given goal in a given situation. AI planning is model-based: a planning system takes as input a description (or model) of the initial situation, the actions available to change it, and the goal condition to output a plan composed of those actions that will accomplish the goal when executed from the initial situation. The Planning Domain Definition Language (PDDL) is a formal knowledge representation language designed to express planning models. Developed by the planning research community as a means of facilitating ...
Structure and Inference in Classical Planning
Classical planning is the problem of finding a sequence of actions for achieving a goal from an initial state assuming that actions have deterministic effects. The most effective approach for finding such plans is based on heuristic search guided by heuristics extracted automatically from the problem representation. In this thesis, we introduce alternative approaches for performing inference over the structure of planning problems that do not appeal to heuristic functions, nor to reductions to other formalisms such as SAT or CSP. We show that many of the standard benchmark domains can be solved with almost no search or a polynomially bounded amount of search, once the structure of planning problems is taken into account. In certain cases we can characterize this structure in terms of a novel width parameter for classical planning.
Computational Complexity of some Optimization Problems in Planning
Automated planning is known to be computationally hard in the general case. Propositional planning is PSPACE-complete and first-order planning is undecidable. One method for analyzing the computational complexity of planning is to study restricted subsets of planning instances, with the aim of differentiating instances with varying complexity. We use this methodology for studying the computational complexity of planning. Finding new tractable (i.e. polynomial-time solvable) problems has been a particularly important goal for researchers in the area. The reason behind this is not only to differentiate between easy and hard planning instances, but also to use polynomial-time solvable instances...
Advanced Topics in Artificial Intelligence
The 12th Australian Joint Conference on Artificial Intelligence (AI'QQ) held in Sydney, Australia, 6-10 December 1999, is the latest in a series of annual re gional meetings at which advances in artificial intelligence are reported. This series now attracts many international papers, and indeed the constitution of the program committee reflects this geographical diversity. Besides the usual tutorials and workshops, this year the conference included a companion sympo sium at which papers on industrial appUcations were presented. The symposium papers have been published in a separate volume edited by Eric Tsui. Ar99 is organized by the University of New South Wales, and sponsored by the Aus tr...
AI 2009: Advances in Artificial Intelligence
This book constitutes the refereed proceedings of the 22nd Australasian Joint Conference on Artificial Intelligence, AI 2009, held in Melbourne, Australia, in December 2009. The 68 revised full papers presented were carefully reviewed and selected from 174 submissions. The papers are organized in topical sections on agents; AI applications; computer vision and image processing; data mining and statistical learning; evolutionary computing; game playing; knowledge representation and reasoning; natural language and speech processing; soft computing; and user modelling.
Reducing Planning Problems by Path Reduction
Planning is among the characteristic features of intelligence and therefore it is a central research topic of Intellectics since its beginning. Although planning is a very hard task, recent planning systems have achieved an astonishing performance and are applied in various fields. One reason for the success of these systems lies, among others, in the exploitation of structural properties that are present in many but not all problems. The use of such structural properties therefore leads to a specialization on a class of problems. Their exploitation is often conducted by a preprocessing step, i.e., by the application of a special algorithm prior to the search for a plan. This work identifies and examines the class of c-invariants as such a structural property of planning problems. c-Invariants are state invariants and are present in many problems of practical interest. Building on the features of c-invariants, the dissertation presents path reduction, a preprocessing technique that can significantly simplify planning problems. Finally, the work describes an implementation of path reduction and examines its application.
Introduction to Graph Neural Networks
Graphs are useful data structures in complex real-life applications such as modeling physical systems, learning molecular fingerprints, controlling traffic networks, and recommending friends in social networks. However, these tasks require dealing with non-Euclidean graph data that contains rich relational information between elements and cannot be well handled by traditional deep learning models (e.g., convolutional neural networks (CNNs) or recurrent neural networks (RNNs)). Nodes in graphs usually contain useful feature information that cannot be well addressed in most unsupervised representation learning methods (e.g., network embedding methods). Graph neural networks (GNNs) are proposed...
Recent Advances in AI Planning
This book constitutes the thoroughly refereed post-proceedings of the 5th European Conference on Planning, ECP'99, held in Durham, UK, in September 1999. The 27 revised full papers presented together with one invited survey were carefully reviewed and selected for inclusion in the book. They address all current aspects of AI planning and scheduling. Several prominent planning paradigms are represented, including planning as satisfiability and other model checking strategies, planning as heuristic state-space search, and Graph-plan-based approaches. Moreover, various new scheduling approaches and combinations of planning and scheduling methods are introduced.
Principles and Practice of Constraint Programming - CP 2010
The 16th annual International Conference on the Principles and Practice of Constraint Programming (CP 2010) was held in St. Andrews, Scotland, during September 6–10, 2010. We would like to thank our sponsors for their generous support of this event. This conference is concerned with all aspects of computing with constraints, including:theory,algorithms,applications,environments,languages,modelsand systems. We received a wide variety of submissions, each of which was reviewed by at least three referees. Referees were chosen for each submission by an initial bidding process where Program Committee members chose papers from their area of interest. The range of expertise represented by the lar...
