Multi-agent Behavior Composition through Adaptable Software Architectures and Tangible Interfaces

2011 ◽  
pp. 278-290 ◽  
Author(s):  
Gabriele Randelli ◽  
Luca Marchetti ◽  
Francesco Antonio Marino ◽  
Luca Iocchi
Keyword(s):  
Software Architectures ◽  
Tangible Interfaces ◽  
Agent Behavior ◽  
Multi Agent

The Effectiveness of Transferring Multi-Agent Behaviors From a Learning Environment in the Presence of Synthetic Social Structures

2005 ◽  
Author(s):  
Keith Garfield ◽  
Annie Wu ◽  
Mehmet Onal ◽  
Britt Crawford ◽  
Adam Campbell ◽  
...  
Keyword(s):  
Learning Environment ◽  
Autonomous Vehicles ◽  
Social Structures ◽  
Agent Behavior ◽  
The Social ◽  
Multi Agent ◽  
Group Members ◽  
The Common ◽  
Primary Focus ◽  
The Impact

The diverse behavior representation schemes and learning paradigms being investigated within the robotics community share the common feature that successful deployment of agents requires that behaviors developed in a learning environment are successfully applied to a range of unfamiliar and potentially more complex operational environments. The intent of our research is to develop insight into the factors facilitating successful transfer of behaviors to the operational environments. We present experimental results investigating the effects of several factors for a simulated swarm of autonomous vehicles. Our primary focus is on the impact of Synthetic Social Structures, which are guidelines directing the interactions between agents, much like social behaviors direct interactions between group members in the human and animal world. The social structure implemented is a dominance hierarchy, which has been shown previously to facilitate negotiation between agents. The goal of this investigation is to investigate mechanisms adding robustness to agent behavior.

Multi-Agent Societies for Collaborative Interaction

1996 ◽  
Vol 40 (15) ◽  
pp. 762-766 ◽  
Author(s):  
Robin R. Penner
Keyword(s):  
Process Management ◽  
Social Structures ◽  
Management Systems ◽  
Interface Model ◽  
Agent Architecture ◽  
Situated Action ◽  
Agent Behavior ◽  
Agent Societies ◽  
Multi Agent ◽  
Collaborative Interaction

The application of a multi-agent architecture to the design and operation of automated process management systems is proving to be a fruitful method of facilitating human-system collaboration. The agent architecture we are developing is intended to be applied in environments where humans and automated systems jointly perform information intensive tasks, and is based on an organization of multiple agents, where both human and software agents are integrated members in groups akin to human societies. Important features of our architecture include an organization based on social structures, a user interface model based on a collaborative interaction metaphors, and a situated action paradigm for agent behavior.

A Mobile Agent Based Approach for Business Processes Automation

2013 ◽  
Vol 1 (2) ◽  
pp. 1-10
Author(s):  
Ernesto López-Mellado ◽  
Marina Flores-Badillo
Keyword(s):  
Mobile Agent ◽  
Business Processes ◽  
Systems Approach ◽  
Workflow Management ◽  
Multi Agent Systems ◽  
Distributed Software ◽  
Agent Based ◽  
Agent Behavior ◽  
Key Issues ◽  
Multi Agent

The paper addresses specification and development of large and complex management systems for business process based on a multi agent systems approach. A methodology for obtaining workflow specifications is presented; it is based on conceiving the management system as a mobile agent system in which mobile agents guide the workflow processes within the organization. The specification includes the work environment, the agent behavior, the process plans, the tasks, the resources, and the interaction protocols. The obtained descriptions are modular and hierarchal leading to clear and compact structuring of the distributed software. The design methodology includes a guideline for Java based coding. Finally, key issues for extending the agent based method to address inter-organizational workflow management are overviewed.

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.

A Simulation of Temporally Variant Agent Interaction via Belief Promulgation

2012 ◽  
pp. 913-927
Author(s):  
Adam J. Conover
Keyword(s):  
Message Passing ◽  
Belief Propagation ◽  
Autonomous Agent ◽  
Emergent Properties ◽  
Agent Behavior ◽  
Temporal Variance ◽  
Agent Interaction ◽  
Multi Agent ◽  
Asynchronous Environments ◽  
Global Population

This chapter concludes a two part series which examines the emergent properties of multi-agent communication in “temporally asynchronous” environments. Many traditional agent and swarm simulation environments divide time into discrete “ticks” where all entity behavior is synchronized to a master “world clock”. In other words, all agent behavior is governed by a single timer where all agents act and interact within deterministic time intervals. This discrete timing mechanism produces a somewhat restricted and artificial model of autonomous agent interaction. In addition to the behavioral autonomy normally associated with agents, simulated agents should also have “temporal autonomy” in order to interact realistically. This chapter focuses on the exploration of a grid of specially embedded, message-passing agents, where each message represents the communication of a core “belief”. Here, we focus our attention on the how the temporal variance of belief propagation from individual agents induces emergent and dynamic effects on a global population.

Modeling the behavior of a hierarchy of command agents with context-based reasoning

2017 ◽  
Vol 15 (4) ◽  
pp. 369-381 ◽  
Author(s):  
Rikke Amilde Løvlid ◽  
Solveig Bruvoll ◽  
Karsten Brathen ◽  
Avelino Gonzalez
Keyword(s):  
Multi Agent System ◽  
Agent System ◽  
Agent Behavior ◽  
Two Agents ◽  
Computer Generated Forces ◽  
Working Together ◽  
Multi Agent

Context-based reasoning is a paradigm for modeling agent behavior that is based on the idea that humans only use a small portion of their knowledge at any given time. It was specially designed to represent human tactical behavior and has been successfully implemented in systems with single agents or two agents working together. In this paper, we apply this idea in a hierarchical multi-agent system of command agents, where the agents’ actions are to command and coordinate subordinates, send reports to their superiors, and communicate with other agents at the same level. We focus on how contexts and actions can be defined for these higher level command agents and how the contexts and actions for the different command agents are related. The proposed methodology is implemented and tested for a hierarchy of command agents that are interpreting and planning an operational order at a battalion level and carrying it out in a computer generated forces environment.

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