K-swaps: cooperative negotiation for solving task-allocation problems | Semantic Scholar (2024)

The results show that allocating clusters of tasks to robots in solving these types of problems is a fast and effective method and produces near optimal solutions.

This overview paper describes the recent progress towards creating a framework for the design and analysis of cooperative auctions for agent coordination, and promises to be efficient both in communication and computation.

This paper extends previous work on sequential single-cluster auctions to handle this more complex task allocation problem for online tasks requiring pickup and delivery and shows that global reallocation of all uncompleted tasks outperforms local replanning in minimising robot path distances.

This paper enhances a recent anytime optimal assignment method based on a task-swap mechanism, redesigning the algorithm to address task allocation problems in a decentralized fashion, needing neither global broadcast nor a multi-hop communication protocol.

This paper enhances a recent anytime optimal assignment method based on a task-swap mechanism, redesigning the algorithm to address task allocation problems in a decentralized fashion, needing neither global broadcast nor a multi-hop communication protocol.

The project on the Analysis, Evaluation and Improvement of Sequential Single- Item Auctions for the Cooperative Real-Time Allocation of Tasks designed efficient and effective auction mechanisms and analyzed their properties.

This paper uses group-theoretic methods to find a minimum-length sequence of adjacent task swappings needed from a source task assignment to a target task assignment in a task swapping network of several well-known topologies.

The contribution of this work is to present an efficient SCA for heterogeneous teams, based on a modified Swendsen-Wang method, that maintains the efficiency of the second SCA and can yield similar performance to the baseline CSA in far fewer iterations.

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It is demonstrated empirically that the team cost of sequential bundle-bid single-sale (= single-item) auction algorithms can be substantially smaller than that without bundles for multi-agent routing problems with capacity constraints.

This paper proposes a completely distributed architecture, where robots dynamically allocate their tasks while they are building their plans, and addresses the problem raised by temporal constraints between tasks by dynamically specifying temporary hierarchies among the tasks.

OCSMcontracts is introduced which combine the ideas behind the four earlier types into an atomic contract type which leads to any sequence of IR contracts leading to the optimal task allocation in a finite number of steps.

To construct negotiation protocols that lead to good allocations quickly, original (O), cluster (C), swap (S), and multiagent (M) contracts were evaluated experimentally and led to the highest social welfare when the ratio of agents to tasks was large, and C-contracts were best when that ratio was small.

This work introduces a novel economic approach for coordinating robots based on the free market system and initial simulation results indicate the approach is successful at producing effective global plans for a team of several robots performing an interior sensing task.

Results show that global costs can be reduced, and hence task allocation can be improved, utilizing leaders, and this paper investigates the effects of introducing opportunistic optimization with leaders to enhance market-based multirobot coordination.

This paper proposes the first systematic method for deriving bidding rules for given team objectives of a multi-robot exploration task and demonstrates experimentally that the resulting bidding rules indeed exhibit good performance for their respective team objectives and compare favorably to the optimal performance.

An introduction to market-based multirobot coordination is provided, a review and analysis of the state of the art in the field, and a discussion of remaining research challenges are discussed.