An Anytime Algorithm for Simultaneous Coalition Structure Generation and Assignment

Präntare, Fredrik; Heintz, Fredrik

doi:10.1007/978-3-030-03098-8_10

Cited by 9 publications

(11 citation statements)

References 28 publications

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“…Our hybrid algorithm (HY) is designed to combine the redundancy-eliminating capabilities of dynamic programming with the pruning abilities and anytime characteristics of branch-and-bound. In more detail, it incorporates these techniques with the search space presentation based on multiset permutations of integer partitions proposed by [6].…”

Section: The Hybrid Algorithmmentioning

confidence: 99%

“…As shown by [6], it is possible to compute a lower and an upper bound for the value of the best solution in such a subspace without having to evaluate any ordered coalition structures. To accomplish this, first define:…”

Section: The Hybrid Algorithmmentioning

confidence: 99%

“…Central to this endeavor is: i) optimally partitioning the set of agents into disjoint groups-an optimization problem known as coalition structure generation (CSG) [13,11]; and ii) deciding on the teams' individual goals, which can be modelled as a linear assignment problem [2,4]. Combining these problems, and solving them simultaneously, can potentially both reduce a problem's computational complexity and increase the agents' aggregated potential utility and performance [6]. This combined CSG and linear assignment problem is a general case of utilitarian combinatorial assignment, and it is known as simultaneous coalition structure generation and assignment (SCSGA) in the multi-agent research community.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Dynamic Programming for Simultaneous Coalition Structure Generation and Assignment

Präntare

Heintz

2021

PRIMA 2020: Principles and Practice of Multi-Agent Systems

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We present, analyze and benchmark two algorithms for simultaneous coalition structure generation and assignment: one based entirely on dynamic programming, and one anytime hybrid approach that uses branch-and-bound together with dynamic programming. To evaluate the algorithms' performance, we benchmark them against both CPLEX (an industry-grade solver) and the stateof-the-art using difficult randomized data sets of varying distribution and complexity. Our results show that our hybrid algorithm greatly outperforms CPLEX, pure dynamic programming and the current state-of-the-art in all of our benchmarks. For example, when solving one of the most difficult problem sets, our hybrid approach finds optimum in roughly 0.1% of the time that the current best method needs, and it generates 98% efficient interim solutions in milliseconds in all of our anytime benchmarks; a considerable improvement over what previous methods can achieve.

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Section: The Hybrid Algorithmmentioning

confidence: 99%

Section: The Hybrid Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Hybrid Dynamic Programming for Simultaneous Coalition Structure Generation and Assignment

Präntare

Heintz

2021

PRIMA 2020: Principles and Practice of Multi-Agent Systems

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“…Note that this paper is a significantly extended and thoroughly revised version of two previous papers [28,29]. More specifically, in this paper, we provide a more thorough review of related algorithms and domains together with examples and descriptions of a few potential applications.…”

Section: Introductionmentioning

confidence: 99%

“…There are many approaches to this, including task allocation [15], assignment algorithms [9,25,27,50], task specification trees [12,23], multi-agent reinforcement learning [38,42], and coalition formation [21,37]. The latter is a paradigm for coordination that has received extensive coverage in the literature over the past two decades [33,35], and typically involves both forming coalitions (flat goal-oriented organizations of agents) and allocating tasks, with potential applications in many disciplines, including economics [51], sensor fusion [10], waste-water treatment systems [11], wireless networks [18], strategy games [28], and small cell networks [54].…”

Section: Introductionmentioning

confidence: 99%

An anytime algorithm for optimal simultaneous coalition structure generation and assignment

Präntare

Heintz

2020

Auton Agent Multi-Agent Syst

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An important research problem in artificial intelligence is how to organize multiple agents, and coordinate them, so that they can work together to solve problems. Coordinating agents in a multi-agent system can significantly affect the system's performance-the agents can, in many instances, be organized so that they can solve tasks more efficiently, and consequently benefit collectively and individually. Central to this endeavor is coalition formation-the process by which heterogeneous agents organize and form disjoint groups (coalitions). Coalition formation often involves finding a coalition structure (an exhaustive set of disjoint coalitions) that maximizes the system's potential performance (e.g., social welfare) through coalition structure generation. However, coalition structure generation typically has no notion of goals. In cooperative settings, where coordination of multiple coalitions is important, this may generate suboptimal teams for achieving and accomplishing the tasks and goals at hand. With this in mind, we consider simultaneously generating coalitions of agents and assigning the coalitions to independent alternatives (e.g., tasks/goals), and present an anytime algorithm for the simultaneous coalition structure generation and assignment problem. This combinatorial optimization problem has many real-world applications, including forming goal-oriented teams. To evaluate the presented algorithm's performance, we present five methods for synthetic problem set generation, and benchmark the algorithm against the industry-grade solver CPLEX using randomized data sets of varying distribution and complexity. To test its anytimeperformance, we compare the quality of its interim solutions against those generated by a greedy algorithm and pure random search. Finally, we also apply the algorithm to solve the problem of assigning agents to regions in a major commercial strategy game, and show that it can be used in game-playing to coordinate smaller sets of agents in real-time.

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Towards Utilitarian Combinatorial Assignment with Deep Neural Networks and Heuristic Algorithms

Präntare

Tiger

Bergström

et al. 2021

Trustworthy AI - Integrating Learning, Optimization and Reasoning

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An Anytime Algorithm for Simultaneous Coalition Structure Generation and Assignment

Cited by 9 publications

References 28 publications

Hybrid Dynamic Programming for Simultaneous Coalition Structure Generation and Assignment

Hybrid Dynamic Programming for Simultaneous Coalition Structure Generation and Assignment

An anytime algorithm for optimal simultaneous coalition structure generation and assignment

Towards Utilitarian Combinatorial Assignment with Deep Neural Networks and Heuristic Algorithms

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