scholarly journals Deliberative Agents in Dynamic Environments, Using Jason and NARS

2021 ◽  
Author(s):  
valeria seidita ◽  
francesco lanza ◽  
Patrick Hammer ◽  
Antonio Chella ◽  
Pei Wang
Keyword(s):  
Dynamic Environments ◽  
General Intelligence ◽  
Multi Agent Systems ◽  
Bdi Agents ◽  
Agent Systems ◽  
Intelligence System ◽  
Artificial General Intelligence ◽  
Reasoning System ◽  
Multi Agent ◽  
High Level

This work explore the possibility to combine the Jason reasoning cycle with a Non-Axiomatic Reasoning System (NARS) to develop multi-agent systems that are able to reason, deliberate and plan when information about plans to be executed and goals to be pursued is missing or incomplete. The contribution of this work is a method for BDI agents to create high-level plans using an AGI (Artificial General Intelligence) system based on non-axiomatic logic.

scholarly journals Deliberative Agents in Dynamic Environments, Using Jason and NARS

2021 ◽  
Author(s):  
valeria seidita ◽  
francesco lanza ◽  
Patrick Hammer ◽  
Antonio Chella ◽  
Pei Wang
Keyword(s):  
Dynamic Environments ◽  
General Intelligence ◽  
Multi Agent Systems ◽  
Bdi Agents ◽  
Agent Systems ◽  
Intelligence System ◽  
Artificial General Intelligence ◽  
Reasoning System ◽  
Multi Agent ◽  
High Level

This work explore the possibility to combine the Jason reasoning cycle with a Non-Axiomatic Reasoning System (NARS) to develop multi-agent systems that are able to reason, deliberate and plan when information about plans to be executed and goals to be pursued is missing or incomplete. The contribution of this work is a method for BDI agents to create high-level plans using an AGI (Artificial General Intelligence) system based on non-axiomatic logic.

Rule Engines and Agent-Based Systems

2012 ◽  
pp. 211-218 ◽  
Author(s):  
Agostino Poggi ◽  
Michele Tomaiuolo
Keyword(s):  
Dynamic Environments ◽  
Multi Agent Systems ◽  
Rule Engine ◽  
Rule Based ◽  
Agent Based ◽  
Agent Systems ◽  
Loosely Coupled ◽  
Domain Specific ◽  
Multi Agent ◽  
Good Support

Expert systems are successfully applied to a number of domains. Often built on generic rule-based systems, they can also exploit optimized algorithms. On the other side, being based on loosely coupled components and peer to peer infrastructures for asynchronous messaging, multi-agent systems allow code mobility, adaptability, easy of deployment and reconfiguration, thus fitting distributed and dynamic environments. Also, they have good support for domain specific ontologies, an important feature when modelling human experts’ knowledge. The possibility of obtaining the best features of both technologies is concretely demonstrated by the integration of JBoss Rules, a rule engine efficiently implementing the Rete-OO algorithm, into JADE, a FIPA-compliant multi-agent system.

An overview of search strategies in distributed environments

2013 ◽  
Vol 29 (3) ◽  
pp. 281-313 ◽  
Author(s):  
E. Del Val ◽  
M. Rebollo ◽  
V. Botti
Keyword(s):  
Dynamic Environments ◽  
System Structure ◽  
Search Process ◽  
Global Information ◽  
Distributed Environments ◽  
Multi Agent Systems ◽  
Agent Systems ◽  
Service Oriented ◽  
Multi Agent ◽  
Proper Performance

AbstractDistributed systems are populated by a large number of heterogeneous entities that join and leave the systems dynamically. These entities act as clients and providers and interact with each other in order to get a resource or to achieve a goal. To facilitate the collaboration between entities, the system should provide mechanisms to manage the information about which entities or resources are available in the system at a certain moment, as well as how to locate them in an efficient way. However, this is not an easy task in open and dynamic environments where there are changes in the available resources and global information is not always available. In this paper, we present a comprehensive vision of search in distributed environments. This review not only considers the approaches of the peer-to-peer area, but also the approaches from three more areas: service-oriented environments, multi-agent systems, and complex networks. In these areas, the search for resources, services, or entities plays a key role for the proper performance of the systems built on them. The aim of this analysis is to compare approaches from these areas taking into account the underlying system structure and the algorithms or strategies that participate in the search process.

scholarly journals Special Issue “Multi-Agent Systems”: Editorial

2019 ◽  
Vol 9 (5) ◽  
pp. 954 ◽  
Author(s):  
Stefano Mariani ◽  
Andrea Omicini
Keyword(s):  
Everyday Life ◽  
Dynamic Environments ◽  
Multi Agent Systems ◽  
Special Issue ◽  
Agent Based ◽  
Agent Systems ◽  
Agent Based Modelling ◽  
Manufacturing Management ◽  
Multi Agent ◽  
Technical Systems

Multi-agent systems (MAS) allow and promote the development of distributed and intelligent applications in complex and dynamic environments. Applications of this kind have a crucial role in our everyday life, as witnessed by the broad range of domains they are deployed to—such as manufacturing, management sciences, e-commerce, biotechnology, etc. Despite heterogeneity, those domains share common requirements such as autonomy, structured interaction, mobility, and openness—which are well suited for MAS. Therein, in fact, goal-oriented processes can enter and leave the system dynamically and interact with each other according to structured protocols. This special issue gathers 17 contributions spanning from agent-based modelling and simulation to applications of MAS in situated and socio-technical systems.

scholarly journals Using Human-Agent Teams to Purposefully Design Multi-Agent Systems

2019 ◽  
Vol 63 (1) ◽  
pp. 1425-1429 ◽  
Author(s):  
Christopher Flathmann ◽  
Nathan McNeese ◽  
Lorenzo Barberis Canonico
Keyword(s):  
Dynamic Environments ◽  
Design Model ◽  
Individual Member ◽  
Multi Agent Systems ◽  
Dynamic Nature ◽  
Agent Systems ◽  
Model Based ◽  
Human Agent ◽  
Multi Agent ◽  
Agent Teams

With multi-agent teams becoming more of a reality every day, it is important to create a common design model for multi-agent teams. These teams need to be able to function in dynamic environments and still communicate with any humans that may need a problem solved. Existing human-agent research can be used to purposefully create multi-agent teams that are interdependent but can still interact with humans. Rather than creating dynamic agents, the most effective way to overcome the dynamic nature of modern workloads is to create a dynamic team configuration, rather than individual member-agents that can change their roles. Multi-agent teams will require a variety of agents to be designed to cover a diverse subset of problems that need to be solved in the modern workforce. A model based on existing multi-agent teams that satisfies the needs of human-agent teams has been created to serve as a baseline for human-interactive multi-agent teams.

Dimensions in programming multi-agent systems

2019 ◽  
Vol 34 ◽  
Author(s):  
Olivier Boissier ◽  
Rafael H. Bordini ◽  
Jomi F. Hübner ◽  
Alessandro Ricci
Keyword(s):  
Programming Model ◽  
Environment Interaction ◽  
Multi Agent Systems ◽  
Bdi Agents ◽  
Agent Systems ◽  
Programming Abstractions ◽  
Multi Agent

Abstract Research on Multi-Agent Systems (MAS) has led to the development of several models, languages, and technologies for programming not only agents, but also their interaction, the application environment where they are situated, as well as the organization in which they participate. Research on those topics moved from agent-oriented programming towards multi-agent-oriented programming (MAOP). A MAS program is then designed and developed using a structured set of concepts and associated first-class design and programming abstractions that go beyond the concepts normally associated with agents. They include those related to environment, interaction, and organization. JaCaMo is a platform for MAOP built on top of three seamlessly integrated dimensions (i.e. structured sets of concepts and associated execution platforms): for programming belief desire intention (BDI) agents, their artefact-based environments, and their normative organizations. The key purpose of our work on JaCaMo is to support programmers in exploring the synergy between these dimensions, providing a comprehensive programming model, as well as a corresponding platform for developing and running MAS. This paper provides a practical overview of MAOP using JaCaMo. We show how emphasizing one particular dimension leads to different solutions to the same problem, and discuss the issues of each of those solutions.

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