Artificial Intelligence Planning for Ambient Environments

2011 ◽  
pp. 195-225 ◽  
Author(s):  
J. Bidot ◽  
S. Biundo
Keyword(s):  
Artificial Intelligence ◽  
Artificial Intelligence Planning

scholarly journals What you always wanted to know about the deterministic part of the International Planning Competition (IPC) 2014 (but were too afraid to ask)

2018 ◽  
Vol 33 ◽  
Author(s):  
Mauro Vallati ◽  
Lukáš Chrpa ◽  
Thomas L. Mccluskey
Keyword(s):  
Artificial Intelligence ◽  
State Of The Art ◽  
The State ◽  
Strong Impact ◽  
Significant Progress ◽  
Planning Techniques ◽  
Artificial Intelligence Planning ◽  
The Future ◽  
International Planning ◽  
Planning Approaches

AbstractThe International Planning Competition (IPC) is a prominent event of the artificial intelligence planning community that has been organized since 1998; it aims at fostering the development and comparison of planning approaches, assessing the state-of-the-art in planning and identifying new challenging benchmarks. IPC has a strong impact also outside the planning community, by providing a large number of ready-to-use planning engines and testing pioneering applications of planning techniques.This paper focusses on the deterministic part of IPC 2014, and describes format, participants, benchmarks as well as a thorough analysis of the results. Generally, results of the competition indicates some significant progress, but they also highlight issues and challenges that the planning community will have to face in the future.

Comparing HTN Planning Method With Grammar-Based Approach in Generative Design

2000 ◽  
Author(s):  
Hai Shi ◽  
Linda C. Schmidt
Keyword(s):  
Artificial Intelligence ◽  
Conceptual Design ◽  
Planning Method ◽  
Future Research ◽  
Planning Problem ◽  
Generative Design ◽  
Artificial Intelligence Planning ◽  
Design Alternatives ◽  
Htn Planning ◽  
Initial Comparison

Abstract In mechanical conceptual design, the more design alternatives generated, the higher the benefit to designers. In this paper we explore the use of HTN planning, an artificial intelligence planning method, to perform generative conceptual design. The HTN planning method is “goal driven” while the grammar method is “feasibility driven”. We mapped a grammar-based generative method for conceptual design of Meccano carts into an HTN planning problem format. An initial comparison of the two methods is provided in this paper. Exploring the use of a planning method provides a benchmark for future research in generative design.

Pipesworld: Applying Planning Systems to Pipeline Transportation

2004 ◽  
Author(s):  
Ruy L. Milidiu´ ◽  
Frederico dos Santos Liporace
Keyword(s):  
Artificial Intelligence ◽  
Transportation Problem ◽  
General Purpose ◽  
Benchmark Problems ◽  
Planning Problem ◽  
Due Dates ◽  
Planning Systems ◽  
Pipeline Transportation ◽  
Specific Knowledge ◽  
Artificial Intelligence Planning

Most transportation problems consist of moving carriers of stationary cargo. Pipelines are unique in the sense that they are stationary carriers of moving cargo. As a consequence, the planning problem of these systems has singularities that make it very challenging. In this paper we present the Pipesworld model, a transportation problem inspired by the transportation of petroleum derivatives in Petrobras’ pipelines. Pipesworld takes into account important features like product interface constraints, limited product storage capacities and due dates for product delivery. The relevance and unique characteristics of Pipesworld has been recognized by the Artificial Intelligence planning community. Pipesworld has been selected as one of the benchmark problems to be used in the Fourth International Planning Competition, a biannual event to benchmark the state-of-the-art general purpose artificial planning systems. We report the results obtained by general purpose artificial intelligence planning systems when applied to the Pipesworld instances. We also analyze how different modelling techniques may be used to significantly improve the planners’ performance. Although the basic algorithms of these planners do not incorporate any specific knowledge about the pipeline transportation problem, the results obtained so far are quite satisfactory. We also describe our current work in developing Plumber, a dedicated solver, aimed to tackle effective operational situations. Plumber uses general purpose planning techniques but also incorporates domain specific knowledge and may work together with a human expert during the planning process. By applying Plumber to the Pipesworld instances, we compare its performance against general purpose planning systems. Preliminary tests with a first version of Plumber shows that it already outperforms Fast-Forward (FF), one of the best available general purpose planning systems. This shows that improved versions of Plumber have the potential to effectively deal with pipeline transportation operational scenarios.

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