This paper proposes a new model and algorithm for task sequencing in remote laser welding in the automotive industry. It is shown that task sequencing (in which order to weld the seams) is strongly related to path planning (how the welding robot should move), therefore the two problems must be solved together, in an integrated way. The problem is modeled as a direct product of a traveling salesman and a path planning problem, and a tabu search algorithm is proposed for solving it. Computational experiments show that the proposed method leads to a substantial reduction in the cycle time of the welding operation compared to an earlier approach.

Unit Commitment is a fundamental problem in power systems engineering, deciding which generating units to switch on, and when to switch them on, in order to efficiently meet anticipated demand. It has traditionally been solved as a Mixed Integer Programming (MIP) problem but upcoming changes to the power system drastically increase the MIP solution time. In this paper, we discuss the benefits that using planning may have over the established methods.We provide a formal description of Unit Commitment, and we present its formulation as MIP and as a planning problem. This is a novel and interesting application area for planning, with features that make the domain challenging for current planners.

Goal-driven automated composition of software components is an important problem with applications in Web service composition and stream processing systems. The popular approach to address this problem is to build the composition automatically using AI planning. However, it is shown that some of these planning approaches may neither be feasible nor scalable for many large-scale flow-based applications. Recent advances have proven that the automated composition problem can take advantage of expert knowledge describing the many ways in which different reusable components can be composed. This knowledge can be represented using an extensible composition template or pattern. In prior work, a flow pattern language called Cascade and its corresponding specialized planner have shown the best performance in these domains. In this paper, we propose the use of Hierarchical Task Network (HTN) planning for the composition of stream processing applications. To this end, we propose an automated approach of creating an HTN-based problem from the Cascade representation of the flow patterns. The resulting technique not only allows us to use the HTN planning paradigm and its many advantages including added expressivity but also enables optimization and customization of composition with respect to preferences and constraints. Further, we propose and develop a lookahead heuristic and show that it significantly reduces the planning time. We have performed extensive experimentation with stream processing applications and evaluated applicability and performance of our approach.

The electricity network balancing problem consists of ensuring that the electricity demands of the consumers are met by the committed supply. Constraints are imposed on the different elements of the network, so that damage to the equipment is prevented when transformers are stepped up or down, or generation is increased. We consider this problem within zones, which are sub-networks constructed using carefully chosen decomposition principles. The automation of decision making in electricity networks is a step forward in their management which is necessary for coping with the increase in power system complexity that we expect in the near term. In this paper we explore the deployment of planning techniques to solve the zone-balancing problem. Embedding electricity networks in a domain description presents new challenges for planning. The key point is that the propagation of information requires complex updates to the state when an action is applied. We have developed a method in which the computation of the critical numeric quantities is performed calling an external power flow equation solver, demonstrating a clean interface between the planner and this domain-specific computation. This solver allows us to move the power flow computations outside of the planning process and update the values efficiently. We also examine a second important feature of this problem, which is the interaction between exogenous events and constraints over the entire plan trajectory within a zone.

Search And Tracking (SAT) is the problem of searching for a mobile target and tracking it after it is found. As this problem has important applications in search-and-rescue and surveillance operations, recently there has been increasing interest in equipping unmanned aerial vehicles (UAVs) with autonomous SAT capabilities. State-of-the-art approaches to SAT rely on estimating the probability density function of the target's state and solving the search control problem in a greedy fashion over a short planning horizon (typically, a one-step lookahead). These techniques suffer high computational cost, making them unsuitable for complex problems. In this paper, we propose a novel approach to SAT, which allows us to handle big geographical areas, complex target motion models and long-term operations. Our solution is to track the target reactively while it is in view and to plan a recovery strategy that relocates the target every time it is lost, using a high-performing automated planning tool. The planning problem consists of deciding where to search and which search patterns to use in order to maximise the likelihood of recovering the target. We show experimental results demonstrating the potential of our approach.

In large and complex planning problems, there will almost inevitably be aspects that are not relevant to a specific problem instance. Thus, identifying and removing irrelevant parts from an instance is one of the most important techniques for scaling up automated planning. We examine the path-based relevance analysis method, which is safe (preserves plan existence and cost) and powerful but has exponential time complexity, and show how to make it run in polynomial time with only a minimal loss of pruning power.

All-pairs shortest paths (APSP) can eliminate the need to search in a graph, providing optimal moves very fast. A major challenge is storing pre-computed APSP data efficiently. Recently, compression has successfully been employed to scale the use of APSP data to roadmaps and gridmaps of realistic sizes. We develop new techniques that improve the compression power of state-of-the-art methods by up to a factor of 5. We demonstrate our ideas on game gridmpaps and the roadmap of Australia. Part of our ideas have been integrated in the Copa CPD system, one of the two best optimal participants in the grid-based path planning competition GPPC.

An elegant way to tackle a problem that you cannot solve is to cast it to a problem that you can solve very well. Cyclic Scheduling problems are very similar to Resource Constrained Project Scheduling Problems (RCPSP), except that the project activities are repeated over time. Due to the similarity, reducing Cyclic Scheduling problems to RCPSPs seems an appealing approach. In this paper we discuss four methods to perform the reduction. The first two are existing techniques. The remaining ones are novel and include the first (to the best of our knowledge) equivalent RCPSP formulation of a cyclic problem. We compare the presented approaches in an experimental evaluation.

In CPP, we are given a set of actions (assumed deterministic in this paper), a distribution over initial states, a goal condition, and a real value 0 < θ ≤1. We seek a plan π such that following its execution, the goal probability is at least θ. Motivated by the success of the translation-based approach for conformant planning, introduced by Palacios and Geffner, we suggest a new compilation scheme from CPP to classical planning. Our compilation scheme maps CPP into cost-bounded classical planning, where the cost-bound represents the maximum allowed probability of failure. Empirically, this technique shows mixed, but promising results, performing very well on some domains, and less so on others when compared to the state of the art PFF planner. It is also very flexible due to its generic nature, allowing us to experiment with diverse search strategies developed for classical planning. Our results show that compilation-based technique offer a new viable approach to CPP and, possibly, more general probabilistic planning problems.

The behavior composition problem involves the automatic synthesis of a controller able to “realize” (i.e., implement) a target behavior module by suitably coordinating a collection of partially controllable available behaviors. In this paper, we show that the existence of a composition solution amounts to finding a strong cyclic plan for a special non-deterministic planning problem, thus establishing the formal link between the two synthesis tasks. Importantly, our results support the use of non-deterministic planing systemsfor solving composition problems in an off-the-shelf manner. We then empirically evaluate three state-of-the-art synthesis systems (a domain-independent automated planner and two game solvers based on model checking techniques) on various non-trivial composition instances. Our experiments show that while behavior composition is EXPTIME-complete, the current technology is already able to handle instances of significant complexity. Our work is, as far as we know, the first serious experimental work on behavior composition.