Task Assignment of the Improved Contract Net Protocol under a Multi-Agent System

Abstract

Background: The existing contract net protocol has low overall efficiency during the bidding and release period, and a large amount of redundant information is generated during the negotiation process. Methods: On the basis of an ant colony algorithm, the dynamic response threshold model and the flow of pheromone model were established, then the complete task allocation process was designed. Three experimental settings were simulated under different conditions. Results: When the number of agents was 20 and the maximum load value was L max = 3 , the traffic and run-time of task allocation under the improved contract net protocol decreased. When the number of tasks and L max was fixed, the improved contract net protocol had advantages over the dynamic contract net and classical contract net protocols in terms of both traffic and run-time. Setting up the number of agents, tasks and L max to improve the task allocation under the contract net not only minimizes the number of errors, but also the task completion rate reaches 100%. Conclusions: The improved contract net protocol can reduce the traffic and run-time compared with classical contract net and dynamic contract net protocols. Furthermore, the algorithm can achieve better assignment results and can re-forward all erroneous tasks.

Deep Reinforcement Learning Task Assignment Based on Domain Knowledge

“…Task assignment in game confrontation is achieving optimal resource utilization by subdividing the various aspects of the conflict into multiple tasks and rationalizing the assignment among the different units (e.g., sensors and interceptors). The commonly used methods for solving task assignment are mainly intelligent optimization algorithms such as genetic algorithms [18] and simulated annealing algorithms [19]; swarm intelligence algorithms such as ant colony algorithms [20] and fish swarm algorithms [21], and market mechanism-based methods such as auction algorithms [22] and contract network protocols [23]. With the increasing diversity of sparring situations, methods for generating deterministic strategies gradually fail to meet the demand.…”

A survey of task allocation techniques in MAS

“…In [86] an optimisation and an auction based approach are combined, while in [87] a market based method is combined with a game theory based one. Furthermore, [88], [89] and [13] are a market based and metaheuristic combination and [90] is a market based and learning combination. In [91] an evolutionary algorithm with a greedy algorithm are combined, while in [92] a game theoretic based approach is combined with a learning algorithm.…”

A Dynamic Task Scheduling Method for Multiple UAVs Based on Contract Net Protocol

“…To cope with the issue, Choi et al [21] proposed consensus-based decentralized auctions (CBBA) which delete the auctioneer and are distributed in each UAV, which has attracted a lot of research interest. The method includes two phases: one is the bundle-construction phase where each agent creates just a single bundle and updates it as the assignment process progresses; the other is the conflict resolution phase where agents bid on a single task and release it upon receiving a higher value in the winning bids list [22][23][24]. The two phases are in an iterative manner until the defined stopping criteria are satisfied.…”