A CASE FOR DOMAIN-INDEPENDENT DETERMINISTIC MULTIAGENT

The notion of planning using multiple agents has been around since the very beginning of planning itself. It has been approached from various viewpoints especially in the multiagent systems community. Recently, domain-independent multiagent planning has gained more attention also in the automated planning community. In this paper, we shortly present the current state of the art, question some aspects of the research field and discuss the rising challenges

DECENTRALIZED MULTIAGENT METAREASONING APPLICATIONS IN TASK ALLOCATION AND PATH FINDING

Decentralized task allocation and path finding are two problems for multiagent systems where no single fixed algorithm provides the best solution in all environments. Past research has considered metareasoning approaches to these problems that take in map, multiagent system, or communication information. None of these papers address the application of metareasoning about individual agent state features which could decrease communication and increase performance for decentralized systems. This thesis presents the application of a meta-level policy that is conducted offline using supervised learning through extreme gradient boosting. The multiagent system used here operates under full communication, and the system uses an independent multiagent metareasoning structure. This thesis describes research that developed and evaluated metareasoning approaches for the multiagent task allocation problem and the multiagent path finding problem. For task allocation, the metareasoning policy determines when to run a task allocation algorithm. For multiagent path finding, the metareasoning policy determines which algorithm an agent should use. The results of this comparative research suggest that this metareasoning approach can reduce communication and computational overhead without sacrificing performance

Pathfinding Algorithm Optimization Via Evolution

Pathfinding is a popular computer science problem in both academic research and industrial development. The objective of pathfinding is to search for a path, often the shortest path, from one location to another on a graph. Many real world applications can be considered as pathfinding problems, including motion planning, video games, logistics, and decision making. Computer scientists have proposed different algorithms to efficiently search for the shortest path. A* search algorithm is the de facto pathfinding algorithm that uses a heuristic function to determine the best action to take based on the given information. It is the most popular pathfinding algorithm due to its simplicity and efficiency. The performance of A* is heavily dependent on the quality of the heuristic function. The heuristic function determines the search speed, accuracy, and memory consumption. Hence, designing good heuristic functions for specific domains becomes the primary research focus on pathfinding algorithm optimization. In this dissertation, we address and solve several commonly known challenges in pathfinding problems and A* algorithm. First, designing new heuristic functions is a difficult and time-consuming task, especially when they are used to solve complex problems. The task requires the user to have expert knowledge of the problem. Moreover, a single heuristic function might not be enough to digest all the provided information and return the best guidance during the search. Previous works suggest that multiple heuristics for complex problems can dramatically speed up the search. However, choosing the appropriate combination of heuristic functions is tricky. Current optimization approaches rely on hand-tuning the parameters via trial and error by engineers over many iterations. There is a need to reduce the difficulty of designing heuristic functions for search performance maximization. Our first contribution is to propose an improved A* with a self-evolving heuristic function named Evolutionary Heuristic A* (EHA*) that reduces engineering effort to design the heuristic function for A* and maximize the search performance. Our experiment results show that EHA* (i) preserves path optimality; (ii) is not limited to a particular application; (iii) speeds up the path searching process; and (iv) most importantly, dramatically reduces the difficulty for software engineers to design heuristic functions for A* search. Moreover, our work can be applied to other existing works on the performance improvement of A* search. Search, A* search suffers from poor performance on large search spaces. Although EHA* improves the quality of heuristic functions, large search space still leads to many unnecessary searches. Our second contribution is Regions Discovery Algorithm (RDA), a map clustering technique to partition a grid based map into different categories to reduce search spaces and increase search speed. Our approach reduces the size of search spaces by partitioning a graph into many segments and identifying the segments by their characteristics. By identifying segments in different categories, we can easily eliminate search space, such as rooms, that are not possible (better use needed?) to be part of the optimal solution. Unlike the existing approaches that might result in non-optimal solutions, our experiment results show that RDA guarantees optimal solutions. Our third contribution, the Hierarchical Evolutionary Heuristic A* (HEHA*), further improves the search ability of handling complex pathfinding problems and boosting the search performance, by reducing search spaces and exploiting parallelism techniques. HEHA* combines the strength of EHA* and RDA to reduce search spaces and improve search speed. HEHA* shows that it provides better search performance with less memory consumption. In the pre-processing phase, first HEHA* partitions a graph into different segments and then applies different optimized heuristic functions for each segment to maximize the search performance. During the online process, HEHA* searches on the abstract level first to reduce search area, and exploits parallelism to speed up the search. Fourth, we improve and apply HEHA* to Multi-Agent Pathfinding (MAPF) problems. MAPF is the fundamental problem of many robotic and logistic applications, where the main constraint is that all agents can find the shortest paths while not colliding with each other. While the current trend favors the central controlled system, our approach is to develop a distributed version of HEHA* that can efficiently plan the optimal path for each agent. Such a system requires data sharing and exchanging among the agents, so that each agent can make its own decision without a supervising system. Our experiment results show that the Multi-Agent version of HEHA* maintains a high success rate when the number of agents increases. While EHA* and HEHA* provide a novel approach for heuristic function design, the pre-processing times are not trivial. To boost the performance of the preprocessing steps in EHA* and HEHA*, we propose a FPGA-based reconfigurable hardware accelerator that is not bound to any specific applications as our fifth contribution. Since GA requires many independent processes, it is suitable to implement it in a hardware accelerator to gain maximum performance. We apply the following techniques to enhance performance: deep pipelining, reconfigurable computing, massive parallel processing, and degree of parallelism maximization. Our results show that the FPGA accelerator for EHA* improves the scalability, throughput, and latency

Agent-Based Algorithms for the Vehicle Routing Problem with Time Windows

Vehicle routing problem s casovymi okny (VRPTW) je jednim z nejdulezitSjSich a nejvice zkou- manych problemu v oblasti dopravy. Matematicky model tohoto problemu vystihuje klicove vlastnosti spolecne cele fadS dalslch dopravmch problemu feSenych v praxi. Jadrem problemu je hledani mnoziny tras zacmajicicli a koncicich v jedinem depu, ktere obsahuji zastavky u mnoziny zakazniku. Pro kazdSho zakazm'ka je pak definovano konkretm' mnozstvf zbozf, jez je tfeba dorucit a casove okno, ve kterem je pozadovano dodani tohoto zbozi. Realne aplikace tohoto problemu jsou zpravidla vyrazne bohatsi, napojene na nadfazene logisticke systemy. KliSoA'ym faktorem pro uspSSne nasazeni odpovldajicich algoritmu je proto jejich fiexibilita vzhledem k dodatecnym rozSuemm zhkladmho matematickeho modelu spojenym s nasazenim v realnem sv§t§. Dalglm podstatnym faktorem je schopnost systemu reagovat na nepfedvidane udalosti jako jsou dopravm zaepy, poruchy, zmgny preferenci zakazniku atd. Multi-agentni systemy reprezentuji architekturu a navrhovy vzor vhodny pro modelovani heterogennlch a dynamickych systemu. Entity v systemu jsou v ramci multi-agentmho mo- delu reprezentovany mnozinou agentil s odpovidajlci'mi vzorci autonommho jako i spolecenskeho chovani. Chovani systemu jako celku pak vyplyva z autonomnich akci...The vehicle routing problem with time windows (VRPTW) is one of the most important and widely studied transportation optimization problems. It abstracts the salient features of numer- ous distribution related real-world problems. It is a problem of finding a set of routes starting and ending at a single depot serving a set of geographically scattered customers, each within a specific time-window and with a specific demand of goods to be delivered. The real world applications of the VRPTW can be very complex being part of higher level sj'^stems i.e. complex supply chain management solutions. For a successful deployment it is impor- tant for these systems to be flexible in terms of incorporating the problem specific side-constraints and problem extensions in an elegant way. Also, employing efficient means of addressing the dy- namism inherent to the execution phase of the relevant operations is vital. The multi-agent systems are an emerging architectm-e with respect to modeling multi-actor heterogenous and dynamic environments. The entities within the system are represented by a set of agents endowed with autonomic as well as social behavioral patterns. The behavior of the system then emerges from their actions and interactions. The autonomic nature of such a model makes it very robust in highly...Katedra softwarového inženýrstvíDepartment of Software EngineeringFaculty of Mathematics and PhysicsMatematicko-fyzikální fakult

Cooperative Multi-Agent Planning: A survey

[EN] Cooperative multi-agent planning (MAP) is a relatively recent research field that combines technologies, algorithms, and techniques developed by the Artificial Intelligence Planning and Multi-Agent Systems communities. While planning has been generally treated as a single-agent task, MAP generalizes this concept by considering multiple intelligent agents that work cooperatively to develop a course of action that satisfies the goals of the group. This article reviews the most relevant approaches to MAP, putting the focus on the solvers that took part in the 2015 Competition of Distributed and Multi-Agent Planning, and classifies them according to their key features and relative performance.This work is supported by the GLASS project Grant No. TIN2014-55637-C2-2-R MINECO of the Spanish Ministerio de Economia, Industria y Competitividad, the Prometeo project II/2013/019 funded by the Valencian Government, and the four-year FPI-UPV research scholarship granted to the first author by the Universitat Politecnica de Valencia. Additionally, this research was partially supported by the Czech Science Foundation under Grant No. 15-20433Y CSF.Torreño Lerma, A.; Onaindia De La Rivaherrera, E.; Komenda, A.; Tolba, M. (2017). Cooperative Multi-Agent Planning: A survey. ACM Computing Surveys. 50(6):84:1-84:32. https://doi.org/10.1145/3128584S84:184:32506Eyal Amir and Barbara Engelhardt. 2003. Factored planning. 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