Information Sharing via Active Sensing in a Multi-Agent System Inspired by Echolocating Bats

2014 ◽  
Author(s):  
Yuan Lin ◽  
Nicole Abaid
Keyword(s):  
Information Sharing ◽  
Sensory Information ◽  
Three Dimensions ◽  
Particle Model ◽  
Model Parameters ◽  
Active Sensing ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Sound Environment ◽  
Multi Agent

Current models for multi-agent systems almost exclusively employ sensory modalities such as vision where agents passively receive information from the environment. Active sensing, defined as acquiring environmental information using self-generated signals, allows widespread sharing of sensory information among agents and thus gives rise to more complex interactions within engineered multi-agent systems using radar or sonar, for example. In nature, bat swarms are animal groups that successfully employ active sensing with each individual broadcasting echolocation pulses in the environment and responding to echoes. Bats flying in groups may cope with the dense sound environment through their behavior; one hypothesized strategy is the cessation of echolocation pulses in the presence of peers and “eavesdropping”, which has been demonstrated in controlled laboratory settings. In this work, we build a self-propelled-particle model with each agent avoiding obstacles in three dimensions by emitting echolocation pulses of a unique frequency. We implement a bat-inspired rule of eavesdropping to take advantage of information sharing via active sensing while reducing the energy expenditure of the group. Through a simulation study, we show that agents indeed capitalize on peers’ pulses and echoes for obstacle avoidance and we find a maximum of this effect for a set of model parameters which relate to the domain size.

Society of Agents

2020 ◽  
pp. 160-183
Author(s):  
Steven Walczak
Keyword(s):  
Problem Solving ◽  
Information Sharing ◽  
Multi Agent Systems ◽  
New Classification ◽  
Conceptual Foundation ◽  
Agent Systems ◽  
Multi Agent ◽  
Coordination And Collaboration ◽  
Agent Coordination ◽  
New Framework

The development of multiple agent systems faces many challenges, including agent coordination and collaboration on tasks. Minsky's The Society of Mind provides a conceptual view for addressing these multi-agent system problems. A new classification ontology is introduced for comparing multi-agent systems. Next, a new framework called the Society of Agents is developed from Minsky's conceptual foundation. A Society of Agents framework-based problem-solving and a Game Society is developed and applied to the domain of single player logic puzzles and two player games. The Game Society solved 100% of presented Sudoku and Kakuro problems and never lost a tic-tac-toe game. The advantage of the Society of Agents approach is the efficient re-utilization of agents across multiple independent game domain problems and a centralized problem-solving architecture with efficient cross-agent information sharing.

scholarly journals Cooperative Information Sharing to Improve Distributed Learning in Multi-Agent Systems

2005 ◽  
Vol 24 ◽  
pp. 407-463 ◽  
Author(s):  
P. S. Dutta ◽  
N. R. Jennings ◽  
L. Moreau
Keyword(s):  
Information Sharing ◽  
Formal Analysis ◽  
Empirical Evaluation ◽  
Task Completion ◽  
Multi Agent Systems ◽  
Long Distance ◽  
State Information ◽  
Agent Systems ◽  
Systems Research ◽  
Multi Agent

Effective coordination of agents' actions in partially-observable domains is a major challenge of multi-agent systems research. To address this, many researchers have developed techniques that allow the agents to make decisions based on estimates of the states and actions of other agents that are typically learnt using some form of machine learning algorithm. Nevertheless, many of these approaches fail to provide an actual means by which the necessary information is made available so that the estimates can be learnt. To this end, we argue that cooperative communication of state information between agents is one such mechanism. However, in a dynamically changing environment, the accuracy and timeliness of this communicated information determine the fidelity of the learned estimates and the usefulness of the actions taken based on these. Given this, we propose a novel information-sharing protocol, post-task-completion sharing, for the distribution of state information. We then show, through a formal analysis, the improvement in the quality of estimates produced using our strategy over the widely used protocol of sharing information between nearest neighbours. Moreover, communication heuristics designed around our information-sharing principle are subjected to empirical evaluation along with other benchmark strategies (including Littman's Q-routing and Stone's TPOT-RL) in a simulated call-routing application. These studies, conducted across a range of environmental settings, show that, compared to the different benchmarks used, our strategy generates an improvement of up to 60% in the call connection rate; of more than 1000% in the ability to connect long-distance calls; and incurs as low as 0.25 of the message overhead.

scholarly journals A UNIFIED SPECIFICATION FORMAT FOR INTERLEVEL RELATIONS BETWEEN AGENT MODELS IN MULTIPLE ABSTRACTION DIMENSIONS

2012 ◽  
Vol 04 (01) ◽  
pp. 1250026 ◽  
Author(s):  
JAN TREUR
Keyword(s):  
Three Dimensions ◽  
Multi Agent System ◽  
Multi Agent Systems ◽  
Temporal Abstraction ◽  
Levels Of Abstraction ◽  
Agent Systems ◽  
Different Types ◽  
Multi Agent ◽  
Multiple Levels ◽  
Abstraction Levels

Multi-agent systems for a certain application area can be modeled at multiple levels of abstraction. Interlevel relations are a means to relate models from different abstraction levels. Three dimensions of abstraction often occurring are the process abstraction, temporal abstraction, and agent cluster abstraction dimension. In this paper a unifying formalization is presented that can be used as a framework to specify interlevel relations for any of such dimensions. The approach is illustrated by showing how a variety of different types of abstraction relations between multi-agent system models can be formally specified in a unified manner.

Collaborative Multiagent Systems — an Alternative to Full Automation of Production

2020 ◽  
Vol 21 (7) ◽  
pp. 404-411
Author(s):  
V. V. Serebrenny
Keyword(s):  
Information System ◽  
Sensory Information ◽  
Physical Characteristics ◽  
Multi Agent System ◽  
Multi Agent Systems ◽  
Agent System ◽  
Agent Systems ◽  
Collaborative System ◽  
Multi Agent ◽  
Full Automation

The paper proposes a new approach as an alternative to full automation of processes that meets current economic trends — collaborative multi-agent systems. In this concept, people and robots are considered as agents in a single sensory-information field, who perform tasks to achieve the goals of the collaborative multi-agent system. The urgency of collaborative multi-agent systems results from the fact that the industrial use of fully automated multi-component systems is limited by the financial and infrastructural unavailability of various industries to switch to completely unmanned technologies. The proposed approach combines the latest, but remaining quite recouped, technological advances along with highly skilled human labor. The use of collaborative multi-agent systems will be economically justified in the manufacture of products in small batches, in the conditions of rapid change of product lines, as well as the presence of staff shortages. The article shows that such an approach can significantly reduce automation costs, while ensuring that the specified production indicators are met. This approach allows taking a fresh look at a human, considering him and a robot as equal partners within a collaborative system. The basic concepts and distinctive characteristics of collaborative multi-agent systems are formulated and presented in the work, justifications for their use are given. Creating a new class of collaborative multi-agent systems requires solving a number of problems associated with the interaction of man and robot. The article considers issues related to the work of a person within a collaborative system, with a rational separation of human functions and an automated production system, in accordance with the necessary level of collaboration. The inclusion of a person with his psychoemotional and physical characteristics as an equivalent agent of a multi-agent system causes difficulties in formalizing collaborative multi-agent systems associated with the need to take these features into account and create a sensory-information system. The inclusion of a person with his psychoemotional and physical characteristics as an equivalent agent of a multi-agent system causes difficulties in formalizing collaborative multi-agent systems associated with the need to take these features into account and create a sensory-information system. The paper discusses ways to formalize a collaborative multi-agent system and management approaches.

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