Principles of artificial intelligence
Principles of artificial intelligence
Linear-space best-first search
Artificial Intelligence
Criticizing solutions to relaxed models yields powerful admissible heuristics
Information Sciences: an International Journal
Fast planning through planning graph analysis
Artificial Intelligence
Extending Graphplan to handle uncertainty and sensing actions
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Inferring state constraints for domain-independent planning
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Using regression-match graphs to control search in planning
Artificial Intelligence
Planning as constraint satisfaction: solving the planning graph by compiling it into CSP
Artificial Intelligence
Unifying SAT-based and Graph-based Planning
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Recent Progress in the Design and Analysis of Admissible Heuristic Functions
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Extracting Effective and Admissible State Space Heuristics from the Planning Graph
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
An Iterative Algorithm for Synthesizing Invariants
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
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
Planning as Heuristic Search: New Results
ECP '99 Proceedings of the 5th European Conference on Planning: Recent Advances in AI Planning
GRT: A Domain Independent Heuristic for STRIPS Worlds Based on Greedy Regression Tables
ECP '99 Proceedings of the 5th European Conference on Planning: Recent Advances in AI Planning
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
The automatic inference of state invariants in TIM
Journal of Artificial Intelligence Research
Efficient implementation of the plan graph in STAN
Journal of Artificial Intelligence Research
Challenges in bridging plan synthesis paradigms
IJCAI'97 Proceedings of the 15th international joint conference on Artifical intelligence - Volume 1
Reviving partial order planning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Finding optimal solutions to the twenty-four puzzle
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Finding optimal solutions to Rubik's cube using pattern databases
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
A robust and fast action selection mechanism for planning
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Speeding up the calculation of heuristics for heuristic search-based planning
Eighteenth national conference on Artificial intelligence
Domain-independent temporal planning in a planning-graph-based approach
AI Communications
Learning Control Knowledge for Forward Search Planning
The Journal of Machine Learning Research
State-Based Regression with Sensing and Knowledge
PRICAI '08 Proceedings of the 10th Pacific Rim International Conference on Artificial Intelligence: Trends in Artificial Intelligence
PADL '09 Proceedings of the 11th International Symposium on Practical Aspects of Declarative Languages
Learning from planner performance
Artificial Intelligence
Anytime heuristic search for partial satisfaction planning
Artificial Intelligence
Combining Domain-Independent Planning and HTN Planning: The Duet Planner
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Regression with respect to sensing actions and partial states
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Effective approaches for partial satisfaction (over-subscription) planning
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
State agnostic planning graphs and the application to belief-space planning
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
AltAltp: online parallelization of plans with heuristic state search
Journal of Artificial Intelligence Research
Sapa: a multi-objective metric temporal planner
Journal of Artificial Intelligence Research
Planning graph heuristics for belief space search
Journal of Artificial Intelligence Research
When is temporal planning really temporal?
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Discriminative learning of beam-search heuristics for planning
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Parallelizing state space plans online
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Reviving partial order planning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Translating HTNs to PDDL: a small amount of domain knowledge can go a long way
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Set-structured and cost-sharing heuristics for classical planning
Annals of Mathematics and Artificial Intelligence
Learning Linear Ranking Functions for Beam Search with Application to Planning
The Journal of Machine Learning Research
Towards automatic manipulation action planning for service robots
KI'10 Proceedings of the 33rd annual German conference on Advances in artificial intelligence
Integrating neural networks and logistic regression to underpin hyper-heuristic search
Knowledge-Based Systems
State agnostic planning graphs: deterministic, non-deterministic, and probabilistic planning
Artificial Intelligence
On-line planning and scheduling: an application to controlling modular printers
Journal of Artificial Intelligence Research
A conformant planner based on approximation: CpA(H)
ACM Transactions on Intelligent Systems and Technology (TIST) - Special section on agent communication, trust in multiagent systems, intelligent tutoring and coaching systems
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Most recent strides in scaling up planning have centered around two competing themesdisjunctive planners, exemplified by Graphplan, and heuristic state search planners, exemplified by UNPOP, HSP and HSP-r. In this paper, we present a novel approach for successfully harnessing the advantages of the two competing paradigms to develop planners that are significantly more powerful than either of the approaches. Specifically, we show that the polynomial-time planning graph structure that the Graphplan builds provides a rich substrate for deriving a family of highly effective heuristics for guiding state space search as well as CSP style search. The main leverage provided by the planning graph structure is a systematic and graded way to take subgoal interactions into account in designing state space heuristics. For state space search, we develop several families of heuristics, some aimed at search speed and others at optimality of solutions, and analyze many approaches for improving the cost-quality tradeoffs offered by these heuristics. Our normalized empirical comparisons show that our heuristics handily outperform the existing state space heuristics. For CSP style search, we describe a novel way of using the planning graph structure to derive highly effective variable and value ordering heuristics. We show that these heuristics can be used to improve Graphplan's own backward search significantly. To demonstrate the effectiveness of our approach vis a vis the state-of-the-art in plan synthesis, we present AltAlt, a planner literally cobbled together from the implementations of Graphplan and state search style planners using our theory. We evaluate AltAlt on the suite of problems used in the recent AIPS-2000 planning competition. The results place AltAlt in the top tier of the competition plannersoutperforming both Graphplan based and heuristic search based planners.