ASAP: An Automatic Algorithm Selection Approach for Planning

Despite the advances made in the last decade in automated planning, no planner out- performs all the others in every known benchmark domain. This observation motivates the idea of selecting different planning algorithms for different domains. Moreover, the planners’ performances are affected by the structure of the search space, which depends on the encoding of the considered domain. In many domains, the performance of a plan- ner can be improved by exploiting additional knowledge, for instance, in the form of macro-operators or entanglements. In this paper we propose ASAP, an automatic Algorithm Selection Approach for Planning that: (i) for a given domain initially learns additional knowledge, in the form of macro-operators and entanglements, which is used for creating different encodings of the given planning domain and problems, and (ii) explores the 2 dimensional space of available algorithms, defined as encodings–planners couples, and then (iii) selects the most promising algorithm for optimising either the runtimes or the quality of the solution plans

Towards a Reformulation Based Approach for Efficient Numeric Planning: Numeric Outer Entanglements

Restricting the search space has shown to be an effective approach for improving the performance of automated planning systems. A planner-independent technique for pruning the search space is domain and problem reformulation. Recently, Outer Entanglements, which are relations between planning operators and initial or goal predicates, have been introduced as a reformulation technique for eliminating potential undesirable instances of planning operators, and thus restricting the search space. Reformulation techniques, however, have been mainly applied in classical planning, although many real-world planning applications require to deal with numerical information. In this paper, we investigate the usefulness of reformulation approaches in planning with numerical fluents. In particular, we propose and extension of the notion of outer entanglements for handling numeric fluents. An empirical evaluation, which involves 150 instances from 5 domains, shows promising results

On the Completeness of Replacing Primitive Actions with Macro-actions and its Generalization to Planning Operators and Macro-operators

Automated planning, which deals with the problem of generating sequences of actions, is an emerging research topic due to its potentially wide range of real-world application domains. As well as developing and improving planning engines, the acquisition of domain-specific knowledge is a promising way to improve the planning process. Domain-specific knowledge can be encoded into the modelling language that a range of planning engines can accept. This makes encoding domain-specific knowledge planner-independent, and entails reformulating the domain models and/or problem specifications. While many encouraging practical results have been derived from such reformulation methods (e.g. learning macro-actions), little attention has been paid to the theoretical properties such as completeness (keeping solvability of reformulated problems). In this paper, we focus on a special case – removing primitive actions replaced by macro-actions. We provide a theoretical study and come up with conditions under which it is safe to remove primitive actions, so completeness of reformulation is preserved. We extend this study also for planning operators (actions are instances of operators)

Planning through Automatic Portfolio Configuration: The PbP Approach

In the field of domain-independent planning, several powerful planners implementing different techniques have been developed. However, no one of these systems outperforms all others in every known benchmark domain. In this work, we propose a multi-planner approach that automatically configures a portfolio of planning techniques for each given domain. The configuration process for a given domain uses a set of training instances to: (i) compute and analyze some alternative sets of macro-actions for each planner in the portfolio identifying a (possibly empty) useful set, (ii) select a cluster of planners, each one with the identified useful set of macro-actions, that is expected to perform best, and (iii) derive some additional information for configuring the execution scheduling of the selected planners at planning time. The resulting planning system, called PbP (Portfolio- based Planner), has two variants focusing on speed and plan quality. Different versions of PbP entered and won the learning track of the sixth and seventh International Planning Competitions. In this paper, we experimentally analyze PbP considering planning speed and plan quality in depth. We provide a collection of results that help to understand PbP�s behavior, and demonstrate the effectiveness of our approach to configuring a portfolio of planners with macro-actions

Learning for Classical Planning

This thesis is mainly about classical planning for artificial intelligence (AI). In planning, we deal with searching for a sequence of actions that changes the environment from a given initial state to a goal state. Planning problems in general are ones of the hardest problems not only in the area of AI, but in the whole computer science. Even though classical planning problems do not consider many aspects from the real world, their complexity reaches EXPSPACE-completeness. Nevertheless, there exist many planning systems (not only for classical planning) that were developed in the past, mainly thanks to the International Planning Competitions (IPC). Despite the current planning systems are very advanced, we have to boost these systems with additional knowledge provided by learning. In this thesis, we focused on developing learning techniques which produce additional knowledge from the training plans and transform it back into planning do mains and problems. We do not have to modify the planners. The contribution of this thesis is included in three areas. First, we provided theoretical background for plan analysis by investigating action dependencies or independencies. Second, we provided a method for generating macro-operators and removing unnecessary primitive operators. Experimental evaluation of this...Katedra teoretické informatiky a matematické logikyDepartment of Theoretical Computer Science and Mathematical LogicFaculty of Mathematics and PhysicsMatematicko-fyzikální fakult

Supply Chain Network Design Under Uncertain and Dynamic Demand

Supply chain network design (SCND) identifies the production and distribution resources essential to maximizing a network’s profit. Once implemented, a SCND impacts a network’s performance for the long-term. This dissertation extends the SCND literature both in terms of model scope and solution approach. The SCND problem can be more realistically modeled to improve design decisions by including: the location, capacity, and technology attributes of a resource; the effect of the economies of scale on the cost structure; multiple products and multiple levels of supply chain hierarchy; stochastic, dynamic, and correlated demand; and the gradually unfolding uncertainty. The resulting multistage stochastic mixed-integer program (MSMIP) has no known general purpose solution methodology. Two decomposition approaches—end-of-horizon (EoH) decomposition and nodal decomposition—are applied. The developed EoH decomposition exploits the traditional treatment of the end-of-horizon effect. It rests on independently optimizing the SCND of every node of the last level of the scenario-tree. Imposing these optimal configurations before optimizing the design decisions of the remaining nodes produces a smaller and thus easier to solve MSMIP. An optimal solution results when the discount rate is 0 percent. Otherwise, this decomposition deduces a bound on the optimality-gap. This decomposition is neither SCND nor MSMIP specific; it pertains to any application sensitive to the EoH-effect and to special cases of MSMIP. To demonstrate this versatility, additional computational experiments for a two-stage mixed-integer stochastic program (SMIP) are included. This dissertation also presents the first application of nodal decomposition in both SCND and MSMIP. The developed column generation heuristic optimizes the nodal sub-problems using an iterative procedure that provides a restricted master problem’s columns. The heuristic’s computational efficiency rests on solving the sub-problems independently and on its novel handling of the master problem. Conceptually, it reformulates the master problem to avoid the duality-gap. Technologically, it provides the first application of Leontief substitution flow problems in MSMIP and thereby shows that hypergraphs lend themselves to loosely coupled MSMIPs. Computational results demonstrate superior performance of the heuristic approach and also show how this heuristic still applies when the SCND problem is modeled as a SMIP where the restricted master problem is a shortest-path problem