scholarly journals Evolution of coordination in pairwise and multi-player interactions via prior commitments

2021 ◽  
pp. 105971232199316
Author(s):  
Ndidi Bianca Ogbo ◽  
Aiman Elragig ◽  
The Anh Han
Keyword(s):  
Autonomous Agents ◽  
Evolutionary Game ◽  
Multi Agent Systems ◽  
Coordination Problem ◽  
Mutual Cooperation ◽  
Payoff Structure ◽  
Optimal Coordination ◽  
Selection Intensities ◽  
Collective Benefit ◽  
High Level

Upon starting a collective endeavour, it is important to understand your partners’ preferences and how strongly they commit to a common goal. Establishing a prior commitment or agreement in terms of posterior benefits and consequences from those engaging in it provides an important mechanism for securing cooperation. Resorting to methods from Evolutionary Game Theory (EGT), here we analyse how prior commitments can also be adopted as a tool for enhancing coordination when its outcomes exhibit an asymmetric payoff structure, in both pairwise and multi-party interactions. Arguably, coordination is more complex to achieve than cooperation since there might be several desirable collective outcomes in a coordination problem (compared to mutual cooperation, the only desirable collective outcome in cooperation dilemmas). Our analysis, both analytically and via numerical simulations, shows that whether prior commitment would be a viable evolutionary mechanism for enhancing coordination and the overall population social welfare strongly depends on the collective benefit and severity of competition, and more importantly, how asymmetric benefits are resolved in a commitment deal. Moreover, in multi-party interactions, prior commitments prove to be crucial when a high level of group diversity is required for optimal coordination. The results are robust for different selection intensities. Overall, our analysis provides new insights into the complexity and beauty of behavioural evolution driven by humans’ capacity for commitment, as well as for the design of self-organised and distributed multi-agent systems for ensuring coordination among autonomous agents.

Autonomous agents coordination: Action languages meet CLP() and Linda

2012 ◽  
Vol 13 (2) ◽  
pp. 149-173 ◽  
Author(s):  
AGOSTINO DOVIER ◽  
ANDREA FORMISANO ◽  
ENRICO PONTELLI
Keyword(s):  
Autonomous Agents ◽  
Single Agent ◽  
Shared Knowledge ◽  
Shared Environment ◽  
Planning Problem ◽  
Multi Agent Systems ◽  
Action Languages ◽  
Knowledge Repository ◽  
Multi Agent ◽  
High Level

AbstractThe paper presents a knowledge representation formalism, in the form of a high-levelAction Description Language (ADL)for multi-agent systems, where autonomous agents reason and act in a shared environment. Agents are autonomously pursuing individual goals, but are capable of interacting through a shared knowledge repository. In their interactions through shared portions of the world, the agents deal with problems of synchronization and concurrency; the action language allows the description of strategies to ensure a consistent global execution of the agents’ autonomously derived plans. A distributed planning problem is formalized by providing the declarative specifications of the portion of the problem pertaining to a single agent. Each of these specifications is executable by a stand-alone CLP-based planner. The coordination among agents exploits a Linda infrastructure. The proposal is validated in a prototype implementation developed in SICStus Prolog.

scholarly journals Scalable Fault Tolerant Agent Grooming Environment (SAGE)

2011 ◽  
pp. 143-172
Author(s):  
H. Farooq Ahmad ◽  
Hiroki Suguri
Keyword(s):  
Autonomous Agents ◽  
Fault Tolerant ◽  
The Other ◽  
Autonomous Agent ◽  
Multi Agent Systems ◽  
Agent Based ◽  
Agent Behavior ◽  
Agent Systems ◽  
Multi Agent ◽  
High Level

Multi-agent systems (MAS) advocate an agent-based approach to software engineering based on decomposing problems in terms of decentralized, autonomous agents that can engage in flexible, high-level interactions. This chapter introduces scalable fault tolerant agent grooming environment (SAGE), a second-generation Foundation for Intelligent Physical Agents (FIPA)-compliant multi-agent system developed at NIIT-Comtec, which provides an environment for creating distributed, intelligent, and autonomous entities that are encapsulated as agents. The chapter focuses on the highlight of SAGE, which is its decentralized fault-tolerant architecture that can be used to develop applications in a number of areas such as e-health, e-government, and e-science. In addition, SAGE architecture provides tools for runtime agent management, directory facilitation, monitoring, and editing messages exchange between agents. SAGE also provides a built-in mechanism to program agent behavior and their capabilities with the help of its autonomous agent architecture, which is the other major highlight of this chapter. The authors believe that the market for agent-based applications is growing rapidly, and SAGE can play a crucial role for future intelligent applications development.

scholarly journals A Programming Approach to Collective Autonomy

2021 ◽  
Vol 10 (2) ◽  
pp. 27
Author(s):  
Roberto Casadei ◽  
Gianluca Aguzzi ◽  
Mirko Viroli
Keyword(s):  
Autonomous Vehicles ◽  
Autonomic Computing ◽  
Autonomous Agents ◽  
Programming Approach ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Multi Agent ◽  
Simulated Case ◽  
Complex Settings

Research and technology developments on autonomous agents and autonomic computing promote a vision of artificial systems that are able to resiliently manage themselves and autonomously deal with issues at runtime in dynamic environments. Indeed, autonomy can be leveraged to unburden humans from mundane tasks (cf. driving and autonomous vehicles), from the risk of operating in unknown or perilous environments (cf. rescue scenarios), or to support timely decision-making in complex settings (cf. data-centre operations). Beyond the results that individual autonomous agents can carry out, a further opportunity lies in the collaboration of multiple agents or robots. Emerging macro-paradigms provide an approach to programming whole collectives towards global goals. Aggregate computing is one such paradigm, formally grounded in a calculus of computational fields enabling functional composition of collective behaviours that could be proved, under certain technical conditions, to be self-stabilising. In this work, we address the concept of collective autonomy, i.e., the form of autonomy that applies at the level of a group of individuals. As a contribution, we define an agent control architecture for aggregate multi-agent systems, discuss how the aggregate computing framework relates to both individual and collective autonomy, and show how it can be used to program collective autonomous behaviour. We exemplify the concepts through a simulated case study, and outline a research roadmap towards reliable aggregate autonomy.

Design of Complex Engineering Systems Using Multiagent Coordination

2016 ◽  
Author(s):  
Nicolás F. Soria ◽  
Mitchell K. Colby ◽  
Irem Y. Tumer ◽  
Christopher Hoyle ◽  
Kagan Tumer
Keyword(s):  
Autonomous Agents ◽  
Engineering Systems ◽  
Coordination Problem ◽  
Design Decisions ◽  
Multiagent Coordination ◽  
Trade Offs ◽  
Potential System ◽  
Complex Engineering ◽  
System States ◽  
Complex Engineering Systems

In complex engineering systems, complexity may arise by design, or as a by-product of the system’s operation. In either case, the root cause of complexity is the same: the unpredictable manner in which interactions among components modify system behavior. Traditionally, two different approaches are used to handle such complexity: (i) a centralized design approach where the impacts of all potential system states and behaviors resulting from design decisions must be accurately modeled; and (ii) an approach based on externally legislating design decisions, which avoid such difficulties, but at the cost of expensive external mechanisms to determine trade-offs among competing design decisions. Our approach is a hybrid of the two approaches, providing a method in which decisions can be reconciled without the need for either detailed interaction models or external mechanisms. A key insight of this approach is that complex system design, undertaken with respect to a variety of design objectives, is fundamentally similar to the multiagent coordination problem, where component decisions and their interactions lead to global behavior. The design of a race car is used as the case study. The results of this paper demonstrate that a team of autonomous agents using a cooperative coevolutionary algorithm can effectively design a Formula racing vehicle.

A Game Theoretic Approach to Thwart Selfishness Attack in MANET

2012 ◽  
Vol 433-440 ◽  
pp. 3944-3948
Author(s):  
Prasenjit Choudhury ◽  
Anita Pal ◽  
Anjali Gupchup ◽  
Krati Budholiya ◽  
Alokparna Banerjee
Keyword(s):  
Ad Hoc ◽  
Theoretic Approach ◽  
Selfish Nodes ◽  
Selfish Behavior ◽  
Mutual Cooperation ◽  
Game Theoretic ◽  
Hoc Networks ◽  
High Level ◽  
The Impact ◽  
Game Theoretic Approach

Ad-hoc networks are attractive, since they can provide a high level of connectivity without the need of a fixed infrastructure. Nodes that are not within the same transmission range communicate through multi-hops, where intermediate nodes act as relays. Mutual cooperation of all the participating nodes is necessary for proper operation of MANET. However, nodes in MANET being battery-constrained, they tend to behave selfishly while forwarding packets. In this paper, we have investigated the security of MANET AODV routing protocol by identifying the impact of selfish nodes on it. It was observed that due to the presence of selfish nodes, packet loss in the network increases and the performance of MANET degrades significantly. Finally a game theoretic approach is used to mitigate the selfishness attack. All the nodes in MANET should cooperate among themselves to thwart the selfish behavior of attacker nodes.

scholarly journals Group Behavior Learning in Multi-Agent Systems Based on Social Interaction Among Agents

2011 ◽  
Vol 15 (7) ◽  
pp. 896-903 ◽  
Author(s):  
Kun Zhang ◽  
◽  
Yoichiro Maeda ◽  
Yasutake Takahashi ◽  
Keyword(s):  
Social Interaction ◽  
Autonomous Agents ◽  
Cooperative Behavior ◽  
Group Behavior ◽  
Learning Ability ◽  
Human Beings ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Multi Agent ◽  
State Of The Environment

Research on multi-agent systems, in which autonomous agents are able to learn cooperative behavior, has been the subject of rising expectations in recent years. We have aimed at the group behavior generation of the multi-agents who have high levels of autonomous learning ability, like that of human beings, through social interaction between agents to acquire cooperative behavior. The sharing of environment states can improve cooperative ability, and the changing state of the environment in the information shared by agents will improve agents’ cooperative ability. On this basis, we use reward redistribution among agents to reinforce group behavior, and we propose a method of constructing a multi-agent system with an autonomous group creation ability. This is able to strengthen the cooperative behavior of the group as social agents.

An Autonomous Agent Approach to Query Optimization in Stream Grids

2012 ◽  
pp. 276-299
Author(s):  
Saikat Mukherjee ◽  
Srinath Srinivasa ◽  
Krithi Ramamritham
Keyword(s):  
Query Optimization ◽  
Autonomous Agents ◽  
Evolutionary Game ◽  
Continuous Optimization ◽  
Autonomous Agent ◽  
Change Strategies ◽  
Stream Data ◽  
Self Interest ◽  
Grid Nodes ◽  
Intractable Problem

Stream grids are wide-area grid computing environments that are fed by a set of stream data sources, and Queries arrive at the grid from users and applications external to the system. The kind of queries considered in this work is long-running continuous (LRC) queries, which are neither short-lived nor infinitely long lived. The queries are “open” from the grid perspective as the grid cannot control or predict the arrival of a query with time, location, required data and query revocations. Query optimization in such an environment has two major challenges, i.e., optimizing in a multi-query environment and continuous optimization, due to new query arrivals and revocations. As generating a globally optimal query plan is an intractable problem, this work explores the idea of emergent optimization where globally optimal query plans emerge as a result of local autonomous decisions taken by the grid nodes. Drawing concepts from evolutionary game theory, grid nodes are modeled as autonomous agents that seek to maximize a self-interest function using one of a set of different strategies. Grid nodes change strategies in response to variations in query arrival and revocation patterns, which is also autonomously decided by each grid node.

An Autonomous Agent Approach to Query Optimization in Stream Grids

2012 ◽  
pp. 407-428
Author(s):  
Saikat Mukherjee ◽  
Srinath Srinivasa ◽  
Krithi Ramamritham
Keyword(s):  
Query Optimization ◽  
Autonomous Agents ◽  
Evolutionary Game ◽  
Continuous Optimization ◽  
Autonomous Agent ◽  
Change Strategies ◽  
Stream Data ◽  
Self Interest ◽  
Grid Nodes ◽  
Intractable Problem

Stream grids are wide-area grid computing environments that are fed by a set of stream data sources, and Queries arrive at the grid from users and applications external to the system. The kind of queries considered in this work is long-running continuous (LRC) queries, which are neither short-lived nor infinitely long lived. The queries are “open” from the grid perspective as the grid cannot control or predict the arrival of a query with time, location, required data and query revocations. Query optimization in such an environment has two major challenges, i.e., optimizing in a multi-query environment and continuous optimization, due to new query arrivals and revocations. As generating a globally optimal query plan is an intractable problem, this work explores the idea of emergent optimization where globally optimal query plans emerge as a result of local autonomous decisions taken by the grid nodes. Drawing concepts from evolutionary game theory, grid nodes are modeled as autonomous agents that seek to maximize a self-interest function using one of a set of different strategies. Grid nodes change strategies in response to variations in query arrival and revocation patterns, which is also autonomously decided by each grid node.

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.”

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