A deductive solution for plan generation
New Generation Computing
Theoretical Computer Science
Generating plans in linear logic I: actions as proofs
Theoretical Computer Science
Generating plans in linear logic: II. A geometry of conjunctive actions
Theoretical Computer Science
The computational complexity of propositional STRIPS planning
Artificial Intelligence
Linear logic: its syntax and semantics
Proceedings of the workshop on Advances in linear logic
The direct simulation of Minsky machines in linear logic
Proceedings of the workshop on Advances in linear logic
Artificial Intelligence - Special volume on planning and scheduling
Complexity, decidability and undecidability results for domain-independent planning
Artificial Intelligence - Special volume on planning and scheduling
Artificial intelligence: a new synthesis
Artificial intelligence: a new synthesis
Decidability of linear affine logic
Information and Computation
Logic-based artificial intelligence
The Detection and Exploitation of Symmetry in Planning Problems
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on 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
Artificial Intelligence - special issue on planning with uncertainty and incomplete information
Weak, strong, and strong cyclic planning via symbolic model checking
Artificial Intelligence - special issue on planning with uncertainty and incomplete information
Mathematical Structures in Computer Science
The computational complexity of probabilistic planning
Journal of Artificial Intelligence Research
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
A critical look at critics in HTN planning
IJCAI'95 Proceedings of the 14th international joint 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
A linear programming heuristic for optimal planning
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
On Linear Logic Planning and Concurrency
Language and Automata Theory and Applications
On linear logic planning and concurrency
Information and Computation
Linear logic as a tool for planning under temporal uncertainty
Theoretical Computer Science
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The typical AI problem is that of making a plan of the actions to be performed by a robot so that the robot could get into a set of final situations, if it started with a certain initial situation. The planning problem is known to be generally very complex. Even within the case of 'well-balanced' actions, strong planning under uncertainty about the effects of actions, or games such as 'Robot against Nature', is EXPTIME-complete. As a result, AI planners are very sensitive to the number of the variables involved in making a plan, the inherent symmetry of the problem, and the nature of the logical formalisms being used. This paper shows that linear logic provides a convenient and adequate tool for representing strong and weak planning problems in non-deterministic domains. A particular focus of this paper is on planning problems with an unbounded number of functionally identical objects. We show that for such problems linear logic is especially effective and leads to a dramatic contraction of the search space from exponential to polynomial in size. We employ the ability of linear logic to reason about multisets, which in this instance are created by identifying several distinct objects as being functionally equivalent for the problem at hand (think of a number of balls, each of which must be moved to some new location - the balls are distinct, but are functionally equivalent for the problem). In linear logic terms, we establish a clear syntactic condition that allows us to show that solving a generic planning problem where there is only one generic object, directly implies a solution to the original real planning problem over several real objects, the isomorphic copies of the generic object. Moreover, this correspondence also guarantees to produce a real solution that works in polynomial time.