A Formal Framework for Reasoning about Agents' Independence in
Self-organizing Multi-agent Systems
- URL: http://arxiv.org/abs/2105.07648v1
- Date: Mon, 17 May 2021 07:32:43 GMT
- Title: A Formal Framework for Reasoning about Agents' Independence in
Self-organizing Multi-agent Systems
- Authors: Jieting Luo, Beishui Liao, John-Jules Meyer
- Abstract summary: This paper proposes a logic-based framework of self-organizing multi-agent systems.
We show that the computational complexity of verifying such a system remains close to the domain of standard ATL.
We also show how we can use our framework to model a constraint satisfaction problem.
- Score: 0.7734726150561086
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Self-organization is a process where a stable pattern is formed by the
cooperative behavior between parts of an initially disordered system without
external control or influence. It has been introduced to multi-agent systems as
an internal control process or mechanism to solve difficult problems
spontaneously. However, because a self-organizing multi-agent system has
autonomous agents and local interactions between them, it is difficult to
predict the behavior of the system from the behavior of the local agents we
design. This paper proposes a logic-based framework of self-organizing
multi-agent systems, where agents interact with each other by following their
prescribed local rules. The dependence relation between coalitions of agents
regarding their contributions to the global behavior of the system is reasoned
about from the structural and semantic perspectives. We show that the
computational complexity of verifying such a self-organizing multi-agent system
remains close to the domain of standard ATL. We then combine our framework with
graph theory to decompose a system into different coalitions located in
different layers, which allows us to verify agents' full contributions more
efficiently. The resulting information about agents' full contributions allows
us to understand the complex link between local agent behavior and system level
behavior in a self-organizing multi-agent system. Finally, we show how we can
use our framework to model a constraint satisfaction problem.
Related papers
- Agent-Oriented Planning in Multi-Agent Systems [54.429028104022066]
We propose a novel framework for agent-oriented planning in multi-agent systems, leveraging a fast task decomposition and allocation process.
We integrate a feedback loop into the proposed framework to further enhance the effectiveness and robustness of such a problem-solving process.
arXiv Detail & Related papers (2024-10-03T04:07:51Z) - Internet of Agents: Weaving a Web of Heterogeneous Agents for Collaborative Intelligence [79.5316642687565]
Existing multi-agent frameworks often struggle with integrating diverse capable third-party agents.
We propose the Internet of Agents (IoA), a novel framework that addresses these limitations.
IoA introduces an agent integration protocol, an instant-messaging-like architecture design, and dynamic mechanisms for agent teaming and conversation flow control.
arXiv Detail & Related papers (2024-07-09T17:33:24Z) - EvoAgent: Towards Automatic Multi-Agent Generation via Evolutionary Algorithms [55.77492625524141]
EvoAgent is a generic method to automatically extend expert agents to multi-agent systems via the evolutionary algorithm.
We show that EvoAgent can automatically generate multiple expert agents and significantly enhance the task-solving capabilities of LLM-based agents.
arXiv Detail & Related papers (2024-06-20T11:49:23Z) - A cooperative strategy for diagnosing the root causes of quality requirement violations in multiagent systems [4.710921988115686]
We propose a cooperative strategy focused on the identification of the root causes of quality requirement violations in multiagent systems.
This strategy allows agents to cooperate with each other in order to identify whether these violations come from service providers, associated components, or the communication infrastructure.
arXiv Detail & Related papers (2024-04-18T14:41:33Z) - On the Complexity of Multi-Agent Decision Making: From Learning in Games
to Partial Monitoring [105.13668993076801]
A central problem in the theory of multi-agent reinforcement learning (MARL) is to understand what structural conditions and algorithmic principles lead to sample-efficient learning guarantees.
We study this question in a general framework for interactive decision making with multiple agents.
We show that characterizing the statistical complexity for multi-agent decision making is equivalent to characterizing the statistical complexity of single-agent decision making.
arXiv Detail & Related papers (2023-05-01T06:46:22Z) - Causal Explanations for Sequential Decision-Making in Multi-Agent
Systems [31.674391914683888]
CEMA is a framework for creating causal natural language explanations of an agent's decisions in sequential multi-agent systems.
We show CEMA correctly identifies the causes behind the agent's decisions, even when a large number of other agents is present.
We show via a user study that CEMA's explanations have a positive effect on participants' trust in autonomous vehicles.
arXiv Detail & Related papers (2023-02-21T16:34:07Z) - Multi-Agent Imitation Learning with Copulas [102.27052968901894]
Multi-agent imitation learning aims to train multiple agents to perform tasks from demonstrations by learning a mapping between observations and actions.
In this paper, we propose to use copula, a powerful statistical tool for capturing dependence among random variables, to explicitly model the correlation and coordination in multi-agent systems.
Our proposed model is able to separately learn marginals that capture the local behavioral patterns of each individual agent, as well as a copula function that solely and fully captures the dependence structure among agents.
arXiv Detail & Related papers (2021-07-10T03:49:41Z) - An active inference model of collective intelligence [0.0]
This paper posits a minimal agent-based model that simulates the relationship between local individual-level interaction and collective intelligence.
Results show that stepwise cognitive transitions increase system performance by providing complementary mechanisms for alignment between agents' local and global optima.
arXiv Detail & Related papers (2021-04-02T14:32:01Z) - Modelling Cooperation in Network Games with Spatio-Temporal Complexity [11.665246332943058]
We study the emergence of self-organized cooperation in complex gridworld domains.
Using multi-agent deep reinforcement learning, we simulate an agent society for a variety of plausible mechanisms.
Our methods have implications for mechanism design in both human and artificial agent systems.
arXiv Detail & Related papers (2021-02-13T12:04:52Z) - Multi-Agent Interactions Modeling with Correlated Policies [53.38338964628494]
In this paper, we cast the multi-agent interactions modeling problem into a multi-agent imitation learning framework.
We develop a Decentralized Adrial Imitation Learning algorithm with Correlated policies (CoDAIL)
Various experiments demonstrate that CoDAIL can better regenerate complex interactions close to the demonstrators.
arXiv Detail & Related papers (2020-01-04T17:31:53Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.