4,205 research outputs found
Task-Based Information Compression for Multi-Agent Communication Problems with Channel Rate Constraints
A collaborative task is assigned to a multiagent system (MAS) in which agents
are allowed to communicate. The MAS runs over an underlying Markov decision
process and its task is to maximize the averaged sum of discounted one-stage
rewards. Although knowing the global state of the environment is necessary for
the optimal action selection of the MAS, agents are limited to individual
observations. The inter-agent communication can tackle the issue of local
observability, however, the limited rate of the inter-agent communication
prevents the agent from acquiring the precise global state information. To
overcome this challenge, agents need to communicate their observations in a
compact way such that the MAS compromises the minimum possible sum of rewards.
We show that this problem is equivalent to a form of rate-distortion problem
which we call the task-based information compression. We introduce a scheme for
task-based information compression titled State aggregation for information
compression (SAIC), for which a state aggregation algorithm is analytically
designed. The SAIC is shown to be capable of achieving near-optimal performance
in terms of the achieved sum of discounted rewards. The proposed algorithm is
applied to a rendezvous problem and its performance is compared with several
benchmarks. Numerical experiments confirm the superiority of the proposed
algorithm.Comment: 13 pages, 9 figure
Multiagent Bidirectionally-Coordinated Nets: Emergence of Human-level Coordination in Learning to Play StarCraft Combat Games
Many artificial intelligence (AI) applications often require multiple
intelligent agents to work in a collaborative effort. Efficient learning for
intra-agent communication and coordination is an indispensable step towards
general AI. In this paper, we take StarCraft combat game as a case study, where
the task is to coordinate multiple agents as a team to defeat their enemies. To
maintain a scalable yet effective communication protocol, we introduce a
Multiagent Bidirectionally-Coordinated Network (BiCNet ['bIknet]) with a
vectorised extension of actor-critic formulation. We show that BiCNet can
handle different types of combats with arbitrary numbers of AI agents for both
sides. Our analysis demonstrates that without any supervisions such as human
demonstrations or labelled data, BiCNet could learn various types of advanced
coordination strategies that have been commonly used by experienced game
players. In our experiments, we evaluate our approach against multiple
baselines under different scenarios; it shows state-of-the-art performance, and
possesses potential values for large-scale real-world applications.Comment: 10 pages, 10 figures. Previously as title: "Multiagent
Bidirectionally-Coordinated Nets for Learning to Play StarCraft Combat
Games", Mar 201
Analysis and design of multiagent systems using MAS-CommonKADS
This article proposes an agent-oriented methodology called MAS-CommonKADS and develops a case study. This methodology extends the knowledge engineering methodology CommonKADSwith techniquesfrom objectoriented and protocol engineering methodologies. The methodology consists of the development of seven models: Agent Model, that describes the characteristics of each agent; Task Model, that describes the tasks that the agents carry out; Expertise Model, that describes the knowledge needed by the agents to achieve their goals; Organisation Model, that describes the structural relationships between agents (software agents and/or human agents); Coordination Model, that describes the dynamic relationships between software agents; Communication Model, that describes the dynamic relationships between human agents and their respective personal assistant software agents; and Design Model, that refines the previous models and determines the most suitable agent architecture for each agent, and the requirements of the agent network
Multiagent cooperation for solving global optimization problems: an extendible framework with example cooperation strategies
This paper proposes the use of multiagent cooperation for solving global optimization problems through the introduction of a new multiagent environment, MANGO. The strength of the environment lays in itsflexible structure based on communicating software agents that attempt to solve a problem cooperatively. This structure allows the execution of a wide range of global optimization algorithms described as a set of interacting operations. At one extreme, MANGO welcomes an individual non-cooperating agent, which is basically the traditional way of solving a global optimization problem. At the other extreme, autonomous agents existing in the environment cooperate as they see fit during run time. We explain the development and communication tools provided in the environment as well as examples of agent realizations and cooperation scenarios. We also show how the multiagent structure is more effective than having a single nonlinear optimization algorithm with randomly selected initial points
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