IPSS: A Hybrid Approach to Planning and Scheduling Integration

Recently, the areas of planning and scheduling in artificial intelligence (AI) have witnessed a big push toward their integration in order to solve complex problems. These problems require both reasoning on which actions are to be performed as well as their precedence constraints (planning) and the...

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Bibliographic Details
Published in:IEEE transactions on knowledge and data engineering 2006-12, Vol.18 (12), p.1681-1695
Main Authors: Rodriguez-Moreno, M.D., Oddi, A., Borrajo, D., Cesta, A.
Format: Article
Language:English
Subjects:
Agents (artificial intelligence)
Applied sciences
Artificial intelligence
Availability
Computer science
control theory
systems
constraint satisfaction problem
Data processing. List processing. Character string processing
Exact sciences and technology
Information systems. Data bases
Memory organisation. Data processing
Performance evaluation
Planning
Reagents
Reasoning
Resource management
Scheduling
Software
Solvers
Strips
Studies
Tasks
Temporal logic
temporal reasoning
Time factors
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Summary:Recently, the areas of planning and scheduling in artificial intelligence (AI) have witnessed a big push toward their integration in order to solve complex problems. These problems require both reasoning on which actions are to be performed as well as their precedence constraints (planning) and the reasoning with respect to temporal constraints (e.g., duration, precedence, and deadline); those actions should satisfy the resources they use (scheduling). This paper describes IPSS (integrated planning and scheduling system), a domain independent solver that integrates an AI planner that synthesizes courses of actions with constraint-based techniques that reason based upon time and resources. IPSS is able to manage not only simple precedence constraints, but also more complex temporal requirements (as the Allen primitives) and multicapacity resource usage/consumption. The solver is evaluated against a set of problems characterized by the use of multiple agents (or multiple resources) that have to perform tasks with some temporal restrictions in the order of the tasks or some constraints in the availability of the resources. Experiments show how the integrated reasoning approach improves plan parallelism and gains better makespans than some state-of-the-art planners where multiple agents are represented as additional fluents in the problem operators. It also shows that IPSS is suitable for solving real domains (i.e., workflow problems) because it is able to impose temporal windows on the goals or set a maximum makespan, features that most of the planners do not yet incorporate
ISSN:1041-4347
1558-2191
DOI:10.1109/TKDE.2006.191