Manufacturing industries are undergoing a major paradigm shift towards more autonomy. Automated planning and scheduling then becomes a necessity. The Planning and Execution Competition for Logistics Robots in Simulation held at ICAPS is based on this scenario and provides an interesting testbed. However, the posed problem is challenging as also demonstrated by the somewhat weak results in 2017. The domain requires temporal reasoning and dealing with uncertainty. We propose a novel planning system based on Answer Set Programming and the Clingo solver to tackle these problems and incentivize robot cooperation. Our results show a significant performance improvement, both, in terms of lowering computational requirements and better game metrics.

The development of a large number of domain-independentplanners is leading to the use of planning engines in a widerange of applications. This is despite the complexity issues inherent in plan generation, which are exacerbated by the separation of planner logic from domain knowledge. However, this separation supports the use of reformulation and configuration techniques, which transform the model representation in order to improve the planner's performance. In this paper, we investigate how the performance of domain-independent planners can be improved by problem model configuration. We introduce a fully automated method for this configuration task, that considers problem-specific aspects extracted by exploiting a problem- and domain-independent representation of the instance. Our extensive experimental analysis shows that this reformulation technique can have a significant impact on planners' performance.

Conformance checking is the problem of verifying if the actual executions of business processes, which are recorded by information systems in dedicated event logs, are compliant with a process model that encodes the process' constraints. Within conformance checking, alignment-based techniques can exactly pinpoint where deviations are observed. Existing alignment-based techniques rely on the assumption of a perfect knowledge of the order with which process' activities were executed in reality. However, experience shows that, due to logging errors and inaccuracies, it is not always possible to determine the exact order with which certain activities were executed. This paper illustrates an alignment-based technique where the perfect knowledge assumption of the execution's order is removed. The technique transforms the problem of alignment-based conformance checking into a planning problem encoded in PDDL, for which planners can find a correct solution in a finite amount of time. We implemented the technique as a software tool that is integrated with state-of-the-art planners. To showcase its practical relevance and scalability, we report on experiments with a real-life case study and several synthetic ones of increasing complexity.

This paper describes how explicit resource reasoning is added within an existing temporal planning framework that uses timelines as plan representation. The work is grounded on a recent formalization of timeline-based planning that here is extended to model and reason over resources. The formal account is then fully implemented as an extension of the PLATINUm planner and tested to demonstrate its new capabilities.

Path planning on grid maps has progressed significantly in recent years, partly due to the Grid-based Path Planning Competition GPPC. In this work we present an optimal approach which combines features from two modern path planning systems, SRC and JPS, both of which were among the strongest entrants at the 2014 edition of the competition. Given a current state s and a target state t, SRC is used as an oracle to provide an optimal move from s towards t. Once a direction is available we invoke a second JPS-based oracle to tell us for how many steps that move can be repeated, with no need to query the oracles between these steps. Experiments on a range of grid maps demonstrate a strong improvement from our combined approach. Against SRC, which remains an optimal solver with state-of-the-art speed, the performance improvement of our new system ranges from comparable to more than one order of magnitude faster.

Relaxed reachability analysis is relevant to efficient grounding, invariant synthesis as well as the computation of relaxation-based heuristics. Planning domains are typically specified in a lifted representation, where the size of the tasks grows exponentially with the number of objects in the world. This growth also affects the analysis of relaxed reachability. We present a task reduction based on symmetries of the lifted representation that allows to perform the same analysis on smaller tasks.

Chatterjee et al. have recently shown the utility of SAT in solving a class of planning problems with partial observability. A core component of their logical formulation of planning is constraints expressing s-t-reachability in directed graphs. In this work, we show that the scalability of the approach can be dramatically improved by using dedicated graph constraints, and that a far broader class of important planning problems can be expressed in terms of s-t-reachability and acyclicity constraints.

HTN planning combines actions that cause state transition with grammar-like decomposition of compound tasks that additionally restricts the structure of solutions. There are mainly two strategies to solve such planning problems: decomposition-based search in a plan space and progression-based search in a state space. Existing progression-based systems do either not rely on heuristics (e.g. SHOP2) or calculate their heuristics based on extended or modified models (e.g.

We present a generalisation of CPCES, a conformant planner that uses two procedures: candidate plan generation and sampling of the initial belief state. The new CPCES better distinguishes these two procedures and therefore provides a clearer framework for the resolution of conformant planning problems. We study CPCES theoretically by analysing the sampling phase through the lens of tags, width and basis. The benefit of this new interpretation is twofold: firstly it allows us to bound the maximum number of iterations required by CPCES, and second it allows us to individuate sampling strategies that guarantee the discovery of subsets of minimal bases. An experimental analysis reported in the paper shows that the greedy sampling (the original version of CPCES) is the more effective strategy, coverage wise. However, when either the quality of the plans or the size of the resulting samples is important a more sophisticated sampling is more effective.

Conflict-Based Search (CBS) and its enhancements are among the strongest algorithms for the multi-agent path-finding problem. However,existing variants of CBS do not use any heuristics that estimate future work. In this paper, we introduce different admissible heuristics for CBS by aggregating cardinal conflicts among agents. In our experiments, CBS with these heuristics outperforms previous state-of-the-art CBS variants by up to a factor of five.