Practical planning: extending the classical AI planning paradigm
Practical planning: extending the classical AI planning paradigm
O-Plan: the open planning architecture
Artificial Intelligence
Intelligent scheduling
Automatically generating abstractions for planning
Artificial Intelligence
Hierarchical task network planning: formalization, analysis, and implementation
Hierarchical task network planning: formalization, analysis, and implementation
Universal classical planner: an algorithm for unifying state-space and plan-space planning
New directions in AI planning
Solving linear arithmetic constraints for user interface applications
Proceedings of the 10th annual ACM symposium on User interface software and technology
Hybrid planning for partially hierarchical domains
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
CPlan: a constraint programming approach to planning
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Algorithms for Distributed Constraint Satisfaction: A Review
Autonomous Agents and Multi-Agent Systems
The Detection and Exploitation of Symmetry in Planning Problems
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
The LPSAT Engine & Its Application to Resource Planning
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
RealPlan: Decoupling Causal and Resource Reasoning in Planning
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Extending Planning Graphs to an ADL Subset
ECP '97 Proceedings of the 4th European Conference on Planning: Recent Advances in AI Planning
Ignoring Irrelevant Facts and Operators in Plan Generation
ECP '97 Proceedings of the 4th European Conference on Planning: Recent Advances in AI Planning
Understanding and Extending Graphplan
ECP '97 Proceedings of the 4th European Conference on Planning: Recent Advances in AI Planning
A Time and Resource Problem for Planning Architectures
ECP '97 Proceedings of the 4th European Conference on Planning: Recent Advances in AI Planning
Scaling up Planning by Teasing out Resource Scheduling
ECP '99 Proceedings of the 5th European Conference on Planning: Recent Advances in AI Planning
ECP '99 Proceedings of the 5th European Conference on Planning: Recent Advances in AI Planning
Distributed Constraint Satisfaction Algorithm for Complex Local Problems
ICMAS '98 Proceedings of the 3rd International Conference on Multi Agent Systems
Efficient planning by effective resource reasoning
Efficient planning by effective resource reasoning
Bridging the gap between planning and scheduling
The Knowledge Engineering Review
Planning graph as a (dynamic) CSP: exploiting EBL, DDB and other CSP search techniques in Graphplan
Journal of Artificial Intelligence Research
Development of iterative real-time scheduler to planner feedback
IJCAI'97 Proceedings of the Fifteenth international joint conference on Artifical intelligence - Volume 2
Learning first-order definitions of functions
Journal of Artificial Intelligence Research
The automatic inference of state invariants in TIM
Journal of Artificial Intelligence Research
Fast planning through planning graph analysis
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Planning with sharable resource constraints
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 2
Planning as constraint satisfaction: solving the planning graph by compiling it into CSP
Artificial Intelligence
A service creation environment based on end to end composition of Web services
WWW '05 Proceedings of the 14th international conference on World Wide Web
Integrating planning and scheduling in workflow domains
Expert Systems with Applications: An International Journal
A Preliminary Study on the Relaxation of Numeric Features in Planning
Proceedings of the 2005 conference on Artificial Intelligence Research and Development
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Building applications using end to end composition of web services
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 4
Sapa: a multi-objective metric temporal planner
Journal of Artificial Intelligence Research
Loosely coupled formulations for automated planning: an integer programming perspective
Journal of Artificial Intelligence Research
Synthy: A system for end to end composition of web services
Web Semantics: Science, Services and Agents on the World Wide Web
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In most real-world reasoning problems, planning and scheduling phases are loosely coupled. For example, in project planning, the user comes up with a task list and schedules it with a scheduling tool like Microsoft Project. One can view automated planning in a similar way in which there is an action selection phase where actions are selected and ordered to reach the desired goals, and a resource allocation phase where enough resources are assigned to ensure the successful execution of the chosen actions. On the other hand, most existing automated planners studied in Artificial Intelligence do not exploit this loose-coupling and perform both action selection and resource assignment employing the same algorithm. The current work shows that the above strategy severely curtails the scale-up potential of existing state of the art planners which can be overcome by leveraging the loose coupling. Specifically, a novel planning framework called RealPlan is developed in which resource allocation is de-coupled from planning and is handled in a separate scheduling phase. The scheduling problem with discrete resources is represented as a Constraint Satisfaction Problem (CSP) problem, and the planner and scheduler interact either in a master-slave manner or in a peer-peer relationship. In the former, the scheduler simply tries to assign resources to the abstract causal plan passed to it by the planner and returns success. In the latter, a more sophisticated 隆°multi-module dependency directed backtracking隆卤 approach is used where the failure explanation in the scheduler is translated back to the planner and serves as a nogood to direct planner search. RealPlan not only preserves both the correctness as well as the quality (measured in length) of the plan but also improves efficiency. Moreover, the failure-driven learning of constraints can serve as an elegant and effective approach for integrating planning and scheduling systems. Beyond the context of planner efficiency, the current work can be viewed as an important step towards merging planning with real-world problem solving where plan failure during execution can be resolved by undertaking only necessary resource re-allocation and not complete re-planning.