1,721,126 research outputs found
Robust Execution Strategy for Scheduling Under Uncertainity
Get PDFVARAKANTHAM, Pradeep; CHENG, Shih-Fen ; LIM, Yun Fong</p
Talking Trucks: Decentralized Collaborative Multi-Agent Order Scheduling for Self-Organizing Logistics
No full textLogistics planning is a complex optimization problem involving multiple decision makers. Automated scheduling systems offer support to human planners; however state-of-the-art approaches often employ a centralized control paradigm. While these approaches have shown great value, their application is hindered in dynamic settings with no central authority. Motivated by real-world scenarios, we present a decentralized approach to collaborative multi-agent scheduling by casting the problem as a Distributed Constraint Optimization Problem (DCOP). Our model-based heuristic approach uses message passing with a novel pruning technique to allow agents to cooperate on mutual agreement, leading to a near-optimal solution while offering low computational costs and flexibility in case of disruptions. Performance is evaluated in three real-world field trials with a logistics carrier and compared against a centralized model-free Deep Q-Network (DQN)-based Reinforcement Learning (RL) approach, a Mixed-Integer Linear Programming (MILP)-based solver, and both human and heuristic baselines. The results demonstrate that it is feasible to have virtual agents make autonomous decisions using our DCOP method, leading to an efficient distributed solution. To facilitate further research in Self-Organizing Logistics (SOL), we provide a novel real-life dataset.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Algorithmic
Towards efficient planning for real world partially observable domains
Get PDFIn partial fulfillment of the degree of Doctor of Philosophy (Computer Science)</p
Operator-Potentials in Symbolic Search: From Forward to Bi-Directional Search
No full textSymbolic search using binary decision diagrams is a state-of-the-art technique for cost-optimal planning. Heuristic search in this context has been problematic as even a very informative heuristic can be detrimental in case it induces difficult-to-represent state partitionings. It was recently shown that operator-potential heuristics can address this issue in forward search by computing a numeric potential for each operator corresponding to the change of the heuristic value induced by that operator. Forward search is, however, not the best known variant of symbolic search. Here we investigate the integration with backward and bi-directional search instead. We prove that forward search (distance-to-goal) operator-potential heuristics can be turned into backward search (distance-to-initial-state) heuristics elegantly in this context, by summing the backward search path operator-potentials with the initial state goal-distance estimate. We run exhaustive experiments on IPC benchmarks, showing that significant performance improvements can be obtained over symbolic forward search and other state-of-the-art techniques
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Multi-Agent Path Finding with Temporal Jump Point Search
Get PDFTemporal Jump Point Search (JPST) is a recently introduced algorithm for grid-optimal pathfinding among dynamic temporal obstacles. In this work we consider JPST as a low-level planner in Multi-Agent Path Finding (MAPF). We investigate how the canonical ordering of JPST can negatively impact MAPF performance and we consider several strategies which allow us to overcome these limitations. Experiments show our new CBS/JPST approach can substantially improve on CBS/SIPP, a contemporary and leading method from the area
Closing the data-decisions loop: Deploying artificial intelligence for dynamic resource management
No full text
Beyond Stars - Generalized Topologies for Decoupled Search
No full textDecoupled search decomposes a classical planning task bypartitioning its variables such that the dependencies betweenthe resulting factors form a star topology. In this topology,a single center factor can interact arbitrarily with a set ofleaf factors. The leaves, however, can interact with each otheronly indirectly via the center. In this work, we generalize thisstructural requirement and allow arbitrary topologies. Thecomponents must not overlap, i. e., each state variable is as-signed to exactly one factor, but the interaction between fac-tors is not restricted. We show how this generalization is con-nected to star topologies, which implies the correctness ofdecoupled search with this novel type of decomposition. Weintroduce factoring methods that automatically identify thesetopologies on a given planning task. Empirically, the gener-alized factorings lead to increased applicability of decoupledsearch on standard IPC benchmarks, as well as to superiorperformance compared to known factoring methods
- …
