An introduction to the Planning Domain Definition Language /
Contributor(s): Lipovetzky, Nir [author.] | Magazzeni, Daniele [author.] | Muise, Christian [author.].Material type: BookSeries: Synthesis lectures on artificial intelligence and machine learning: #42.; Synthesis digital library of engineering and computer science: Publisher: [San Rafael, California] : Morgan & Claypool, Description: 1 PDF (xvii, 169 pages) : illustrations (some color).Content type: text Media type: electronic Carrier type: online resourceISBN: 9781627057370.Subject(s): Planning Domain Definition Language | Artificial intelligence | Computational complexity | AI planning | problem modelling | classical planning | numeric planning | temporal planning | hybrid planningDDC classification: 006.3 Online resources: Abstract with links to full text | Abstract with links to resource Also available in print.
|Item type||Current location||Call number||Status||Date due||Barcode||Item holds|
|E books||P K Kelkar Library, IIT Kanpur||Available||EBKE895|
Mode of access: World Wide Web.
System requirements: Adobe Acrobat Reader.
Part of: Synthesis digital library of engineering and computer science.
Includes bibliographical references (pages 151-163) and index.
1. Introduction -- 1.1. What is AI planning? -- 1.2. Planning models -- 1.3. Examples -- 1.4. The origins of PDDL and the scope of this book -- 1.5. Planning systems and modelling tools
2. Discrete and deterministic planning -- 2.1. Domain and problem definition -- 2.2. Plans and plan validity -- 2.3. Notes on PDDL's syntax : the strips fragment -- 2.4. Advanced modelling examples -- 2.5. Expressiveness and complexity
3. More expressive classical planning -- 3.1. Conditional and quantified conditions and effects -- 3.2. Axioms -- 3.3. Preferences and plan quality -- 3.4. State trajectory constraints -- 3.5. Expressiveness and complexity
4. Numeric planning -- 4.1. Numeric planning in PDDL -- 4.2. Numeric plan validity -- 4.3. More modelling examples -- 4.4. Complexity of numeric planning
5. Temporal planning -- 5.1. Durative actions -- 5.2. Planning with predictable events -- 5.3. Temporal plan validity -- 5.4. Combining numeric and temporal planning
6. Planning with hybrid systems -- 6.1. Continuous processes -- 6.2. Exogenous events -- 6.3. Example : the generator -- 6.4. Example : multiple-battery management -- 6.5. Plan validation in hybrid domains
7. conclusion -- 7.1. Other planning PDDL-like languages -- 7.2. The future of PDDL -- A. Online PDDL resources.
Abstract freely available; full-text restricted to subscribers or individual document purchasers.
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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 systems comparison, it has become a de-facto standard input language of many planning systems, although it is not the only modelling language for planning. Several variants of PDDL have emerged that capture planning problems of different natures and complexities, with a focus on deterministic problems. The purpose of this book is two-fold. First, we present a unified and current account of PDDL, covering the subsets of PDDL that express discrete, numeric, temporal, and hybrid planning. Second, we want to introduce readers to the art of modelling planning problems in this language, through educational examples that demonstrate how PDDL is used to model realistic planning problems. The book is intended for advanced students and researchers in AI who want to dive into the mechanics of AI planning, as well as those who want to be able to use AI planning systems without an in-depth explanation of the algorithms and implementation techniques they use.
Also available in print.
Title from PDF title page (viewed on May 3, 2019).