Coordinating Agent Interactions Under Open Environments

2011 ◽  
pp. 1430-1443
Author(s):  
Quan Bai ◽  
Minjie Zhang
Keyword(s):  
Multi Agent Systems ◽  
Social Ability ◽  
Agent Interactions ◽  
Interaction Protocols ◽  
The Social ◽  
Open Environments ◽  
Agent Interaction ◽  
Coloured Petri Net ◽  
Multi Agent ◽  
Rich Interaction

An intelligent agent is a reactive, proactive, autonomous, and social entity. The social ability of an agent is exercised in a multi-agent system (MAS), which constitutes a collection of such agents. Current multi-agent systems mostly work in complex, open, and dynamic environments. In an open environment, many facts, such as domain constraints, agent number, and agent relationships, are not fixed. That brings a lot of difficulties to coordinate agents’ interactions and cooperation. One major problem that impedes agent interaction is that most current agent interaction protocols are not very suitable for open environments. In this chapter, we introduce an approach to ameliorate agent interactions from two perspectives. First, the approach can enable agents to form knowledge “rich” interaction protocols by using ontologies. Second, we use coloured Petri net (CPN) based methods to enable agents to form interaction protocols dynamically, which are more suitable for agent interaction under open environments.

Coordinating Agent Interactions Under Open Environments

2006 ◽  
pp. 52-67 ◽  
Author(s):  
Quan Bai ◽  
Minjie Zhang
Keyword(s):  
Multi Agent Systems ◽  
Social Ability ◽  
Agent Interactions ◽  
Interaction Protocols ◽  
The Social ◽  
Open Environments ◽  
Agent Interaction ◽  
Coloured Petri Net ◽  
Multi Agent ◽  
Rich Interaction

An intelligent agent is a reactive, proactive, autonomous, and social entity. The social ability of an agent is exercised in a multi-agent system (MAS), which constitutes a collection of such agents. Current multi-agent systems mostly work in complex, open, and dynamic environments. In an open environment, many facts, such as domain constraints, agent number, and agent relationships, are not fixed. That brings a lot of difficulties to coordinate agents’ interactions and cooperation. One major problem that impedes agent interaction is that most current agent interaction protocols are not very suitable for open environments. In this chapter, we introduce an approach to ameliorate agent interactions from two perspectives. First, the approach can enable agents to form knowledge “rich” interaction protocols by using ontologies. Second, we use coloured Petri net (CPN) based methods to enable agents to form interaction protocols dynamically, which are more suitable for agent interaction under open environments.

A GENERIC FORMAL FRAMEWORK FOR CONSTRUCTING AGENT INTERACTION PROTOCOLS

2005 ◽  
Vol 15 (01) ◽  
pp. 61-85 ◽  
Author(s):  
BO CHEN ◽  
SAMIRA SADAOUI
Keyword(s):  
Online Auction ◽  
Multi Agent Systems ◽  
Interaction Protocols ◽  
Formal Framework ◽  
Agent Interaction ◽  
Finite State ◽  
Generic Agent ◽  
Multi Agent ◽  
Almost All ◽  
Abstraction Levels

Agent interaction protocols (AIP) design is one of the principal issues for building multi-agent systems. Indeed, the construction of AIP should integrate theories, methodologies and tools. We propose in this paper a unifying framework that provides a generic agent architecture to be reused as well as a methodology to construct and refine AIP specifications in an incremental way. This framework is based on the highly expressive formal language Lotos and its related technologies, such as finite state machines and temporal logics. Hence, the proposed framework also facilitates formal validation and verification of AIP specifications using rigorous tools. We argue that there are three layers of semantics of Lotos specifications that can improve Lotos expressivity in describing agent interaction. Therefore, this framework can describe almost all aspects of agent interaction and at different abstraction levels. In addition, we demonstrate how to generate an online auction protocol from the generic framework, and how to validate and verify this protocol.

Bayesian Agencies in Control

2011 ◽  
pp. 168-181
Author(s):  
Anet Potgieter ◽  
Judith Bishop
Keyword(s):  
Bayesian Networks ◽  
Autonomous Agents ◽  
Probabilistic Reasoning ◽  
Multi Agent Systems ◽  
Agent Architectures ◽  
Analysis And Design ◽  
Agent Systems ◽  
Interaction Protocols ◽  
Multi Agent ◽  
The Individual

Most agent architectures implement autonomous agents that use extensive interaction protocols and social laws to control interactions in order to ensure that the correct behaviors result during run-time. These agents, organized into multi-agent systems in which all agents adhere to predefined interaction protocols, are well suited to the analysis, design and implementation of complex systems in environments where it is possible to predict interactions during the analysis and design phases. In these multi-agent systems, intelligence resides in individual autonomous agents, rather than in the collective behavior of the individual agents. These agents are commonly referred to as “next-generation” or intelligent components, which are difficult to implement using current component-based architectures. In most distributed environments, such as the Internet, it is not possible to predict interactions during analysis and design. For a complex system to be able to adapt in such an uncertain and non-deterministic environment, we propose the use of agencies, consisting of simple agents, which use probabilistic reasoning to adapt to their environment. Our agents collectively implement distributed Bayesian networks, used by the agencies to control behaviors in response to environmental states. Each agency is responsible for one or more behaviors, and the agencies are structured into heterarchies according to the topology of the underlying Bayesian networks. We refer to our agents and agencies as “Bayesian agents” and “Bayesian agencies.”

Beyond Citizen Participation in Planning

2010 ◽  
pp. 195-217 ◽  
Author(s):  
Domenico Camarda
Keyword(s):  
Citizen Participation ◽  
Environmental Planning ◽  
Multi Agent Systems ◽  
Formal Representation ◽  
Agent Interactions ◽  
Planning Methods ◽  
Multi Agent ◽  
Planning Processes ◽  
Continuous Feedback ◽  
Problem Setting

The new complexity of planning knowledge implies innovation of planning methods, in both substance and procedure. The development of multi-agent cognitive processes, particularly when the agents are diverse and dynamically associated to their interaction arenas, may have manifold implications. In particular, interesting aspects are scale problems of distributed interaction, continuous feedback on problem setting, language and representation (formal, informal, hybrid, etc.) differences among agents (Bousquet, Le Page, 2004). In this concern, an increasing number of experiences on multi-agent interactions are today located within the processes of spatial and environmental planning. Yet, the upcoming presence of different human agents often acting au paire with artificial agents in a social physical environment (see, e.g., with sensors or data-mining routines) often suggests the use of hybrid MAS-based approaches (Al-Kodmany, 2002; Ron, 2005). In this framework, the chapter will scan experiences on the setting up of cooperative multi-agent systems, in order to investigate the potentials of that approach on the interaction of agents in planning processes, beyond participatory planning as such. This investigation will reflect on agent roles, behaviours, actions in planning processes themselves. Also, an attempt will be carried out to put down formal representation of supporting architectures for interaction and decision making.

scholarly journals ABSTRACT DATA TYPES FOR KNOWLEDGE REPRESENTATION AND SPECIFICATION OF MULTI-AGENT SYSTEMS

2021 ◽  
Vol 2 (336) ◽  
pp. 48-55
Author(s):  
A. Satybaldiyeva ◽  
A. Ismailova ◽  
R. Moldasheva ◽  
A. Mukhanova ◽  
K. Kadirkulov
Keyword(s):  
Distributed System ◽  
Distributed Algorithm ◽  
Multiagent System ◽  
Social Software ◽  
Multi Agent Systems ◽  
Data Types ◽  
Agent Systems ◽  
Social Ability ◽  
Internal States ◽  
Multi Agent

Distributed system is a group of decentralized interacting executers. Distributed algorithm is the communication protocol for a distributed system that transforms the group into a team to solve some task. Multiagent system is a distributed system that consists of autonomous reactive agents, i.e. executers which internal states can be characterized in terms Believes (B), Desires (D), and Intentions (I). Multiagent algorithm is a distributed algorithm for a multiagent system. The article discusses the basic concepts of agents and multi-agent systems. Also, two problems of multi-agent algorithms for representing knowledge in the context of Social Software Engineering are considered. A number of new multi-agent algorithms are presented, and their correctness is proved. The main characteristics of agents are provided, such as autonomy, proactivity, social ability, and reactivity; also, agents can have such additional characteristics as persistence, reasonability, performance, mobility, personality, and rationality. A number of new multi-agent algorithms are presented, and their correctness is proved. Two statements have been proved for solving RAM and MRP problems. This time we address a social issue of agent anonymity and privacy in these algo-rithms.

Temporally Autonomous Agent Interaction

2011 ◽  
pp. 19-37
Author(s):  
Adam J. Conover ◽  
Robert J. Hammell
Keyword(s):  
Autonomous Agent ◽  
Multi Agent Systems ◽  
Agent Behavior ◽  
Simulated Environments ◽  
Interacting Agents ◽  
Agent Interaction ◽  
Multi Agent ◽  
Global Coordination ◽  
Traditional Approaches ◽  
Emergent Behaviors

This work reflects the results of continuing research into “temporally autonomous” multi-agent interaction. Many traditional approaches to modeling multi-agent systems involve synchronizing all agent activity in simulated environments to a single “universal” clock. In other words, agent behavior is regulated by a global timer where all agents act and interact deterministically in time. However, if the objective of any such simulation is to model the behavior of real-world entities, this discrete timing mechanism yields an artificially constrained representation of actual physical agent interaction. In addition to the behavioral autonomy normally associated with agents, simulated agents must also have temporal autonomy in order to interact realistically. Intercommunication should occur without global coordination or synchronization. To this end, a specialized simulation framework is developed. Several simulations are conducted from which data are gathered and we subsequently demonstrate that manipulation of the timing variable amongst interacting agents affects the emergent behaviors of agent populations.

Hermes

2009 ◽  
pp. 105-139 ◽  
Author(s):  
Christopher Cheong ◽  
Michael Winikoff
Keyword(s):  
Intelligent Agents ◽  
Implementation Process ◽  
Design Processes ◽  
Social Ability ◽  
Design And Implementation ◽  
Agent Interactions ◽  
Agent Platforms ◽  
Multi Agent ◽  
Software Engineers ◽  
Implementation Aspects

Although intelligent agents individually exhibit a number of characteristics, including social ability, flexibility, and robustness, which make them suitable to operate in complex, dynamic, and error-prone environments, these characteristics are not exhibited in multi-agent interactions. For instance, agent interactions are often not flexible or robust. This is due to the traditional message-centric design processes, notations, and methodologies currently used. To address this issue, we have developed Hermes, a goaloriented design methodology for agent interactions which is aimed at being pragmatic for practicing software engineers. Hermes focuses on interaction goals, i.e., goals of the interaction which the agents are attempting to achieve, and results in interactions that are more flexible and robust than messagecentric approaches. In this chapter, we present the design and implementation aspects of Hermes. This includes an explanation of the Hermes design processes, notations, and design artifacts, along with a detailed description of the implementation process which provides a mapping of design artifacts to goal-plan agent platforms, such as Jadex.

MASCARET

2011 ◽  
pp. 1137-1156
Author(s):  
Cédric Buche ◽  
Ronan Querrec ◽  
Pierre De Loor ◽  
Pierre Chevaillier
Keyword(s):  
Social Environment ◽  
Intelligent Tutoring System ◽  
Pedagogical Agents ◽  
Multi Agent Systems ◽  
Training Environment ◽  
Pedagogical Strategies ◽  
Operational Conditions ◽  
The Social ◽  
Social Abilities ◽  
Multi Agent

This study concerns virtual environments for training in operational conditions. The principal developed idea is that these environments are heterogeneous and open multi-agent systems. The MASCARET model is proposed to organize the interactions between agents and to provide them reactive, cognitive and social abilities to simulate the physical and social environment. The physical environment represents, in a realistic way, the phenomena that learners and teachers have to take into account. The social environment is simulated by agents executing collaborative and adaptive tasks. These agents realize, in team, procedures that they have to adapt to the environment. The users participate to the training environment through their avatar. In this article, we explain how we integrated, in MASCARET, models necessary to the creation of Intelligent Tutoring System. We notably incorporate pedagogical strategies and pedagogical actions. We present pedagogical agents. To validate our model, the SÉCURÉVI application for fire fighters’ training is developed.

Sign in / Sign up

Export Citation Format

Share Document