Planning graph heuristics for selecting objectives in over-subscription planning problems

Partial Satisfaction or Over-subscription Planning problems arise in many real world applications. Applications in which the planning agent does not have enough resources to accomplish all of their given goals, requiring plans that satisfy only a subset of them. Solving such partial satisfaction planning (PSP) problems poses several challenges, from new models for handling plan quality to efficient heuristics for selecting the most beneficial goals. In this paper, we extend planning graph-based reachability heuristics with mutex analysis to overcome complex goal interactions in PSP problems. We start by describing one of the most general PSP problems, the PSP NET BENEFIT problem, where actions have execution costs and goals have utilities. Then, we present AltWlt, 1 our heuristic approach augmented with a multiple goal set selection process and mutex analysis. Our empirical studies show that AltWlt is able to generate the most beneficial solutions, while incurring only a small fraction of the cost of other PSP approaches

Planning Graph Based Heuristics for Automated Planning

One of the most successful algorithms in the last few years for solving classical planning problems is Graphplan [3]. This algorithm can be seen as a disjunctive version of forward state space planners. The algorithm has two interleaved phases: a forward phase where a polynomial-time data structure called "planning graph" is incrementally extended, and a backward phase where that planning graph is searched for a solution. In thi

AltAlt-p: Online parallelization of plans with heuristic state search

Despite their near dominance, heuristic state search planners still lag behind disjunctive planners in the generation of parallel plans in classical planning. The reason is that directly searching for parallel solutions in state space planners would require the planners to branch on all possible subsets of parallel actions, thus increasing the branching factor exponentially. We present a variant of our state search planner AltAlt called AltAlt-p which generates parallel plans by using greedy online parallelization of partial plans. The greedy approach is significantly informed by the use of novel distance heuristics that AltAlt-p derives from a graphplan-style planning graph for the problem. While this approach is not guaranteed to provide optimal parallel plans, empirical results show that AltAlt-p is capable of generating good quality parallel plans without losing also the quality in terms of the total number of actions in the solution at a fraction of the time cost incurred by the disjunctive planners. Keywords:Domain-independent planning, scalability in planning, heuristic state search planning, parallel plans, and partial planning

Parallel best-first search algorithms for planning problems on multi-core processors

International audienceThe multiplication of computing cores in modern processor units permits revisiting the design of classical algorithms to improve computational performance in complex application domains. Artificial Intelligence planning is one of those applications where large search spaces require intelligent and more exhaustive search control. In this paper, parallel planning algorithms, derived from best-first search, are proposed for shared memory architectures. The parallel algorithms, based on the asynchronous work pool paradigm, maintain good thread occupancy in multi-core CPUs. All algorithms use one ordered global list of states stored in shared memory from where they select nodes for expansion. A parallel best-first search algorithm that develops new states with depth equal to one is proposed first. Then, we propose an extension of this parallel algorithm that features a diversification strategy in order to escape local minima. We study and analyse a set of computational experiments for problems that come from the International Planning Competition and real-world industry applications. The empirical evaluation shows that the parallel algorithms solve most of the domains efficiently without incurring higher solutions costs. In those problems with partial results, we highlight the potential shortcomings of the proposed approaches for promising future directions

Travel Plans in Public Transit Networks Using Artificial Intelligence Planning Models

Users of public transit networks require tools that generate travel plans to traverse them. The main issue is that public transit networks are time and space dependent. Travel plans depend on the current location of users and transit units, along with a set of user preferences and time restrictions. In this work, we propose the design and development of artificial intelligence (AI) planning models for engineering travel plans for such networks. The proposed models consider temporal actions, bus locations, and user preferences as constraints, to restrict the set of travel plans generated. Our approach decouples model design from algorithm construction, providing a greater level of flexibility and richness of solutions. We also introduce an integer linear programming formulation, and a fast preprocessing procedure, to evaluate the quality of the solutions returned by the proposed planning models. Experimental results show that AI planning models can efficiently generate close to optimal solutions. Furthermore, our analysis identifies user preferences as the most critical factor that increases solution complexity for planning models