ADL: exploring the middle ground between STRIPS and the situation calculus
Proceedings of the first international conference on Principles of knowledge representation and reasoning
The computational complexity of propositional STRIPS planning
Artificial Intelligence
Acting optimally in partially observable stochastic domains
AAAI'94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 2)
Reasoning about knowledge
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Journal of the ACM (JACM)
Artificial Intelligence - Special issue on heuristic search in artificial intelligence
LAO: a heuristic search algorithm that finds solutions with loops
Artificial Intelligence - Special issue on heuristic search in artificial intelligence
Modal logic
Introduction to the Theory of Computation
Introduction to the Theory of Computation
Dynamic Programming and Optimal Control, Two Volume Set
Dynamic Programming and Optimal Control, Two Volume Set
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Polynomial-Length Planning Spans the Polynomial Hierarchy
JELIA '02 Proceedings of the European Conference on Logics in Artificial Intelligence
Some Results on the Complexity of Planning with Incomplete Information
ECP '99 Proceedings of the 5th European Conference on Planning: Recent Advances in AI Planning
Automata-Theoretic Approach to Planning for Temporally Extended Goals
ECP '99 Proceedings of the 5th European Conference on Planning: Recent Advances in AI Planning
Weak, strong, and strong cyclic planning via symbolic model checking
Artificial Intelligence - special issue on planning with uncertainty and incomplete information
On the Semantics of Deliberation in Indigolog—from Theory to Implementation
Annals of Mathematics and Artificial Intelligence
Conformant planning via symbolic model checking and heuristic search
Artificial Intelligence
Introduction to Automata Theory, Languages, and Computation (3rd Edition)
Introduction to Automata Theory, Languages, and Computation (3rd Edition)
Separating the polynomial-time hierarchy by oracles
SFCS '85 Proceedings of the 26th Annual Symposium on Foundations of Computer Science
The equivalence problem for regular expressions with squaring requires exponential space
SWAT '72 Proceedings of the 13th Annual Symposium on Switching and Automata Theory (swat 1972)
Distance estimates for planning in the discrete belief space
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
The FF planning system: fast plan generation through heuristic search
Journal of Artificial Intelligence Research
Planning graph heuristics for belief space search
Journal of Artificial Intelligence Research
The fast downward planning system
Journal of Artificial Intelligence Research
STRIPS: a new approach to the application of theorem proving to problem solving
IJCAI'71 Proceedings of the 2nd international joint conference on Artificial intelligence
Faster heuristic search algorithms for planning with uncertainty and full feedback
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Bounded policy iteration for decentralized POMDPs
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Planning and acting in partially observable stochastic domains
Artificial Intelligence
Conformant planning via heuristic forward search: A new approach
Artificial Intelligence
Strong planning under partial observability
Artificial Intelligence
What is planning in the presence of sensing?
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Probabilistic propositional planning: representations and complexity
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
My brain is full: when more memory helps
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Learning finite-state controllers for partially observable environments
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
Solving POMDPs by searching in policy space
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Algorithms and limits for compact plan representations
Journal of Artificial Intelligence Research
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Conformant planning is used to refer to planning for unobservable problems whose solutions, like classical planning, are linear sequences of operators called linear plans. The term 'conformant' is automatically associated with both the unobservable planning model and with linear plans, mainly because the only possible solutions for unobservable problems are linear plans. In this paper we show that linear plans are not only meaningful for unobservable problems but also for partially-observable problems. In such case, the execution of a linear plan generates observations from the environment which must be collected by the agent during the execution of the plan and used at the end in order to determine whether the goal had been achieved or not; this is the typical case in problems of diagnosis in which all the actions are knowledge-gathering actions. Thus, there are substantial differences about linear plans for the case of unobservable or fully-observable problems, and for the case of partially-observable problems: while linear plans for the former model must conform with properties in state space, linear plans for partially-observable problems must conform with properties in belief space. This differences surface when the problems are allowed to express epistemic goals and conditions using modal logic, and place the plan-existence decision problem in different complexity classes. Linear plans is one extreme point in a discrete spectrum of solution forms for planning problems. The other extreme point is contingent plans in which there is a branch point for every possible observation at each time step, and thus the number of branch points is not bounded a priori. In the middle of the spectrum, there are plans with a bounded number of branch points. Thus, linear plans are plans with zero branch points and contingent plans are plans with unbounded number of branch points. In this work, we lay down foundations and principles for the general treatment of linear plans and plans of bounded branching, and provide exact complexity results for novel decision problems. We also show that linear plans for partially-observable problems are not only of theoretical interest since some challenging real-life problems can be dealt with them.