The computational complexity of propositional STRIPS planning
Artificial Intelligence
Plan reuse versus plan generation: a theoretical and empirical analysis
Artificial Intelligence - Special volume on planning and scheduling
Computational complexity of planning and approximate planning in the presence of incompleteness
Artificial Intelligence
Artificial Intelligence
Industrial Applications of Fuzzy Logic and Intelligent Systems
Industrial Applications of Fuzzy Logic and Intelligent Systems
Methods and Tools for Applied Artificial Intelligence
Methods and Tools for Applied Artificial Intelligence
Applying integer programming to AI planning
The Knowledge Engineering Review
IEEE Intelligent Systems
Simulation of production and transportation planning with uncertainty and risk
WSEAS Transactions on Computers
Routing optimization heuristics algorithms for urban solid waste transportation management
WSEAS Transactions on Computers
A critical assessment of benchmark comparison in planning
Journal of Artificial Intelligence Research
Constructing conditional plans by a theorem-prover
Journal of Artificial Intelligence Research
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
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STRIPS planning is a problem of finding of a set of actions that transform given initial state to desired goal situation. It is hard computational problem. In this work an approximation of STRIPS block world planning by linear programming is shown. The cost of such approach is that algorithm can results in non-interpretable solutions for some initial states (what is followed by assumption P ≠ NP). This is because the discrete domain (truth or false) is transformed to continuous domain (LP program). Additionally two scoring functions have been introduced to estimate quality of the plan. Proposed approach is illustrated by exemplary simulation.