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mPower: A component-based development framework for multi-agent systems to support business processes
One of the obstacles preventing the widespread adoption of multi-agent systems in industry is the difficulty of implementing heterogeneous interactions among participating agents via asynchronous messages. This difficulty arises from the need to understand how to combine elements of various content languages, ontologies, and interaction protocols in order to construct meaningful and appropriate messages. In this paper mPower, a component-based layered framework for easing the development of multi-agent systems, is described, and the facility for customising the components for reuse in similar domains is explained. The framework builds on the JADE-LEAP platform, which provides a homogeneous layer over diverse operating systems and hardware devices, and allows ubiquitous deployment of applications built on multi-agent systems both in wired and wireless environments. The use of the framework to develop mPowermobile , a multi-agent system to support mobile workforces, is reported
Agent Based E-Market: Framework, Design, and Implementation
Attempt has been made to design and develop a complete adoptive Multi Agent System pertaining to merchant brokering stage of Customer Buying Behaviour Model with the intent of appropriate framework. Intelligent agents are autonomous entity which observe and act upon an environment. In general, they are software robots and vitally used in variety of e-Business applications. This paper focuses on the discussions on electronic markets and the adoptive role, which agents can play in information transformation for automating e-market transactions. It is proposed to develop a framework for agent-based electronic markets for buyers and sellers totally with the assistance of software agents.Agent Oriented e-Business, Agent Oriented e-Markets, Buyer/Seller Agents, Java, Multi Agent Systems
Efficient Communication and Coordination for Large-Scale Multi-Agent Systems
The growth of the computational power of computers and the speed of networks has made large-scale multi-agent systems a promising technology. As the number of agents in a single application approaches thousands or millions, distributed computing has become a general paradigm in large-scale multi-agent systems to take the benefits of parallel computing. However, since these numerous agents are located on distributed computers and interact intensively with each other to achieve common goals, the agent communication cost significantly affects the performance of applications. Therefore, optimizing the agent communication cost on distributed systems could considerably reduce the runtime of multi-agent applications. Furthermore, because static multi-agent frameworks may not be suitable for all kinds of applications, and the communication patterns of agents may change during execution, multi-agent frameworks should adapt their services to support applications differently according to their dynamic characteristics.
This thesis proposes three adaptive services at the agent framework level to reduce the agent communication and coordination cost of large-scale multi-agent applications. First, communication locality-aware agent distribution aims at minimizing inter-node communication by collocating heavily communicating agents on the same platform and maintaining agent group-based load sharing. Second, application agent-oriented middle agent services attempt to optimize agent interaction through middle agents by executing application agent-supported search algorithms on the middle agent address space. Third, message passing for mobile agents aims at reducing the time of message delivery to mobile agents using location caches or by extending the agent address scheme with location information. With these services, we have achieved very impressive experimental results in large- scale UAV simulations including up to 10,000 agents. Also, we have provided a formal definition of our framework and services with operational semantics
Collaborative Fault-Identification & Reconstruction in Multi-Agent Systems
The conventional solutions for fault-detection, identification, and
reconstruction (FDIR) require centralized decision-making mechanisms which are
typically combinatorial in their nature, necessitating the design of an
efficient distributed FDIR mechanism that is suitable for multi-agent
applications. To this end, we develop a general framework for efficiently
reconstructing a sparse vector being observed over a sensor network via
nonlinear measurements. The proposed framework is used to design a distributed
multi-agent FDIR algorithm based on a combination of the sequential convex
programming (SCP) and the alternating direction method of multipliers (ADMM)
optimization approaches. The proposed distributed FDIR algorithm can process a
variety of inter-agent measurements (including distances, bearings, relative
velocities, and subtended angles between agents) to identify the faulty agents
and recover their true states. The effectiveness of the proposed distributed
multi-agent FDIR approach is demonstrated by considering a numerical example in
which the inter-agent distances are used to identify the faulty agents in a
multi-agent configuration, as well as reconstruct their error vectors
Fuzzy argumentation for trust
In an open Multi-Agent System, the goals of agents acting on behalf of their owners often conflict with each other. Therefore, a personal agent protecting the interest of a single user cannot always rely on them. Consequently, such a personal agent needs to be able to reason about trusting (information or services provided by) other agents. Existing algorithms that perform such reasoning mainly focus on the immediate utility of a trusting decision, but do not provide an explanation of their actions to the user. This may hinder the acceptance of agent-based technologies in sensitive applications where users need to rely on their personal agents. Against this background, we propose a new approach to trust based on argumentation that aims to expose the rationale behind such trusting decisions. Our solution features a separation of opponent modeling and decision making. It uses possibilistic logic to model behavior of opponents, and we propose an extension of the argumentation framework by Amgoud and Prade to use the fuzzy rules within these models for well-supported decisions
Lyapunov-Based Reinforcement Learning for Decentralized Multi-Agent Control
Decentralized multi-agent control has broad applications, ranging from
multi-robot cooperation to distributed sensor networks. In decentralized
multi-agent control, systems are complex with unknown or highly uncertain
dynamics, where traditional model-based control methods can hardly be applied.
Compared with model-based control in control theory, deep reinforcement
learning (DRL) is promising to learn the controller/policy from data without
the knowing system dynamics. However, to directly apply DRL to decentralized
multi-agent control is challenging, as interactions among agents make the
learning environment non-stationary. More importantly, the existing multi-agent
reinforcement learning (MARL) algorithms cannot ensure the closed-loop
stability of a multi-agent system from a control-theoretic perspective, so the
learned control polices are highly possible to generate abnormal or dangerous
behaviors in real applications. Hence, without stability guarantee, the
application of the existing MARL algorithms to real multi-agent systems is of
great concern, e.g., UAVs, robots, and power systems, etc. In this paper, we
aim to propose a new MARL algorithm for decentralized multi-agent control with
a stability guarantee. The new MARL algorithm, termed as a multi-agent
soft-actor critic (MASAC), is proposed under the well-known framework of
"centralized-training-with-decentralized-execution". The closed-loop stability
is guaranteed by the introduction of a stability constraint during the policy
improvement in our MASAC algorithm. The stability constraint is designed based
on Lyapunov's method in control theory. To demonstrate the effectiveness, we
present a multi-agent navigation example to show the efficiency of the proposed
MASAC algorithm.Comment: Accepted to The 2nd International Conference on Distributed
Artificial Intelligenc
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