AND/OR graph heuristic search methods
Journal of the ACM (JACM)
Automatic verification of finite-state concurrent systems using temporal logic specifications
ACM Transactions on Programming Languages and Systems (TOPLAS)
Graph-Based Algorithms for Boolean Function Manipulation
IEEE Transactions on Computers
Principles of artificial intelligence
Principles of artificial intelligence
The complexity of Markov decision processes
Mathematics of Operations Research
Efficient implementation of a BDD package
DAC '90 Proceedings of the 27th ACM/IEEE Design Automation Conference
Symbolic Boolean manipulation with ordered binary-decision diagrams
ACM Computing Surveys (CSUR)
Conditional nonlinear planning
Proceedings of the first international conference on Artificial intelligence planning systems
Symbolic model checking: 1020 states and beyond
Information and Computation - Special issue: Selections from 1990 IEEE symposium on logic in computer science
Acting optimally in partially observable stochastic domains
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Reasoning about knowledge
An algorithm for probabilistic planning
Artificial Intelligence - Special volume on planning and scheduling
Formal methods: state of the art and future directions
ACM Computing Surveys (CSUR) - Special ACM 50th-anniversary issue: strategic directions in computing research
Fast planning through planning graph analysis
Artificial Intelligence
Planning control rules for reactive agents
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
Using temporal logics to express search control knowledge for planning
Artificial Intelligence
An efficient algorithm for searching implicit AND/OR graphs with cycles
Artificial Intelligence
Optimizing decision trees through heuristically guided search
Communications of the ACM
LAO: a heuristic search algorithm that finds solutions with loops
Artificial Intelligence - Special issue on heuristic search in artificial intelligence
Symbolic Model Checking
Learning Sorting and Decision Trees with POMDPs
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
A Performance Study of BDD-Based Model Checking
FMCAD '98 Proceedings of the Second International Conference on Formal Methods in Computer-Aided Design
Value-Directed Belief State Approximation for POMDPs
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Vector-space Analysis of Belief-state Approximation for POMDPs
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Gridworlds as Testbeds for Planning with Incomplete Information
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
A logic programming approach to knowledge-state planning, II: the DLVk system
Artificial Intelligence
A POMDP formulation of preference elicitation problems
Eighteenth national conference on Artificial intelligence
Planning with a language for extended goals
Eighteenth national conference on Artificial intelligence
Complexity Issues in Markov Decision Processes
COCO '98 Proceedings of the Thirteenth Annual IEEE Conference on Computational Complexity
Weak, strong, and strong cyclic planning via symbolic model checking
Artificial Intelligence - special issue on planning with uncertainty and incomplete information
SAT-based planning in complex domains: concurrency, constraints and nondeterminism
Artificial Intelligence - special issue on planning with uncertainty and incomplete information
Conformant planning via symbolic model checking and heuristic search
Artificial Intelligence
Generating safe assumption-based plans for partially observable, nondeterministic domains
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
OBDD-based universal planning for synchronized agents in non-deterministic domains
Journal of Artificial Intelligence Research
Conformant planning via symbolic model checking
Journal of Artificial Intelligence Research
Planning for contingencies: a decision-based approach
Journal of Artificial Intelligence Research
Constructing conditional plans by a theorem-prover
Journal of Artificial Intelligence Research
Improvements to the evaluation of quantified boolean formulae
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
IJCAI'73 Proceedings of the 3rd international joint conference on Artificial intelligence
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Action representation and partially observable planning using epistemic logic
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Conditional progressive planning under uncertainty
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Heuristic search + symbolic model checking = efficient conformant planning
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Planning in nondeterministic domains under partial observability via symbolic model checking
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Planning as model checking for extended goals in non-deterministic domains
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Planning and acting in partially observable stochastic domains
Artificial Intelligence
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Pushing the envelope: planning, propositional logic, and stochastic search
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Structured solution methods for non-Markovian decision processes
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
SPUDD: stochastic planning using decision diagrams
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Anytime state-based solution methods for decision processes with non-Markovian rewards
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
A Symbolic Model Checking Framework for Safety Analysis, Diagnosis, and Synthesis
Model Checking and Artificial Intelligence
Fast and Informed Action Selection for Planning with Sensing
Current Topics in Artificial Intelligence
Conformant plans and beyond: Principles and complexity
Artificial Intelligence
On the effectiveness of CNF and DNF representations in contingent planning
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
A comprehensive approach to on-board autonomy verification and validation
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Searching with partial belief states in general games with incomplete information
KI'12 Proceedings of the 35th Annual German conference on Advances in Artificial Intelligence
Behavioural description based web service composition using abstraction and refinement
International Journal of Web and Grid Services
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Rarely planning domains are fully observable. For this reason, the ability to deal with partial observability is one of the most important challenges in planning. In this paper, we tackle the problem of strong planning under partial observability in nondeterministic domains: find a conditional plan that will result in a successful state, regardless of multiple initial states, nondeterministic action effects, and partial observability. We make the following contributions. First, we formally define the problem of strong planning within a general framework for modeling partially observable planning domains. Second, we propose an effective planning algorithm, based on and-or search in the space of beliefs. We prove that our algorithm always terminates, and is correct and complete. In order to achieve additional effectiveness, we leverage on a symbolic, bdd-based representation for the domain, and propose several search strategies. We provide a thorough experimental evaluation of our approach, based on a wide selection of benchmarks. We compare the performance of the proposed search strategies, and identify a uniform winner that combines heuristic distance measures with mechanisms that reduce runtime uncertainty. Then, we compare our planner mbp with other state-of-the art-systems. mbp is able to outperform its competitor systems, often by orders of magnitude.