A multi-agent cooperative reasoning system for amalgamated knowledge bases

1997 ◽  
pp. 92-105
Author(s):  
Lifeng He ◽  
Yuyan Chao ◽  
Shohey Kato ◽  
Tetsuo Araki ◽  
Hirohisa Seki ◽  
...  
Keyword(s):  
Knowledge Bases ◽  
Reasoning System ◽  
Multi Agent

scholarly journals Towards developing a cyber-physical multi-agent system for managing precise farms with digital twins of plants

2019 ◽  
pp. 257-261
Author(s):  
Vladimir Laryukhin ◽  
Petr Skobelev ◽  
Oleg Lakhin ◽  
Sergey Grachev ◽  
Vladimir Yalovenko ◽  
...  
Keyword(s):  
A Priori ◽  
Knowledge Bases ◽  
Agent Based ◽  
Plant Soil ◽  
Plant Cultivation ◽  
Technical Requirements ◽  
Digital Twins ◽  
History Of ◽  
Multi Agent ◽  
High Uncertainty

The paper presents the multi-agent approach for developing cyber-physical system for managing precise farms with digital twins of plants. It discusses complexity of the problem caused by a priori incompleteness of knowledge about factors of plant growth and development, high uncertainty of crops cultivation, variety of weather, business and technical requirements, etc. The approach proposes knowledge bases and multi-agent technology in combination with machine learning methods for designing considered systems. Digital twin of plant is specified as an agent based on ontology model of objects relevant for plant cultivation (specific sort of plant, soil, etc) associated with history of operations and environment conditions. The architecture and functions of system components are designed. The expected results of system implementation and the benefits for farmers are discussed.

Design Patterns for Social Intelligent Agent Architectures Implementation

2021 ◽  
pp. 294-319
Author(s):  
Manuel Kolp ◽  
Yves Wautelet ◽  
Samedi Heng
Keyword(s):  
Design Patterns ◽  
Architectural Design ◽  
Autonomous Agents ◽  
Knowledge Bases ◽  
Multi Agent Systems ◽  
Business Systems ◽  
Agent Architectures ◽  
Social Patterns ◽  
Agent Oriented Software Engineering ◽  
Multi Agent

Multi-agent systems (MAS) architectures are popular for building open, distributed, and evolving software required by today's business IT applications such as e-business systems, web services, or enterprise knowledge bases. Since the fundamental concepts of MAS are social and intentional rather than object, functional, or implementation-oriented, the design of MAS architectures can be eased by using social patterns. They are detailed agent-oriented design idioms to describe MAS architectures as composed of autonomous agents that interact and coordinate to achieve their intentions like actors in human organizations. This chapter presents social patterns and focuses on a framework aimed to gain insight into these patterns. The framework can be integrated into agent-oriented software engineering methodologies used to build MAS. The authors consider the broker social pattern to illustrate the framework. The mapping from system architectural design (through organizational architectural styles), to system detailed design (through social patterns), is overviewed with a data integration case study.

Engineering Software Systems with Social-Driven Templates

2010 ◽  
pp. 1-29
Author(s):  
Manuel Kolp ◽  
Yves Wautelet ◽  
Sodany Kiv ◽  
Vi Tran
Keyword(s):  
Architectural Design ◽  
Autonomous Agents ◽  
Knowledge Bases ◽  
Software Systems ◽  
Multi Agent Systems ◽  
Business Systems ◽  
Agent Systems ◽  
Social Patterns ◽  
Engineering Software ◽  
Multi Agent

Multi-Agent Systems (MAS) architectures are gaining popularity over traditional ones for building open, distributed, and evolving software required by today’s corporate IT applications such as e-business systems, Web services or enterprise knowledge bases. Since the fundamental concepts of multi-agent systems are social and intentional rather than object, functional, or implementation-oriented, the design of MAS architectures can be eased by using social-driven templates. They are detailed agent-oriented design idioms to describe MAS architectures as composed of autonomous agents that interact and coordinate to achieve their intentions, like actors in human organizations. This paper presents social patterns, as well as organizational styles, and focuses on a framework aimed to gain insight into these templates. The framework can be integrated into agent-oriented software engineering methodologies used to build MAS. We consider the Broker social pattern to illustrate the framework. The mapping from system architectural design (through organizational architectural styles), to system detailed design (through social patterns), is overviewed with a data integration case study. The automation of patterns design is also overviewed.

Social Structure Based Design Patterns for Agent-Oriented Software Engineering

2009 ◽  
pp. 773-796
Author(s):  
Manuel Kolp ◽  
Stéphane Faulkner ◽  
Yves Wautelet
Keyword(s):  
Software Engineering ◽  
Design Patterns ◽  
Autonomous Agents ◽  
Knowledge Bases ◽  
Multi Agent Systems ◽  
Business Systems ◽  
Agent Systems ◽  
Social Patterns ◽  
Agent Oriented Software Engineering ◽  
Multi Agent

Multi-agent systems (MAS) architectures are gaining popularity over traditional ones for building open, distributed, and evolving software required by today’s corporate IT applications such as e-business systems, Web services, or enterprise knowledge bases. Since the fundamental concepts of multi-agent systems are social and intentional rather than object, functional, or implementationoriented, the design of MAS architectures can be eased by using social patterns. They are detailed agent-oriented design idioms to describe MAS architectures composed of autonomous agents that interact and coordinate to achieve their intentions, like actors in human organizations. This article presents social patterns and focuses on a framework aimed to gain insight into these patterns. The framework can be integrated into agent-oriented software engineering methodologies used to build MAS. We consider the Broker social pattern to illustrate the framework. An overview of the mapping from system architectural design (through organizational architectural styles), to system detailed design (through social patterns), is presented with a data integration case study. The automation of creating design patterns is also discussed.

Chapter 5. Answering Natural-Language Questions with Neuro-Symbolic Knowledge Bases

2021 ◽  
Author(s):  
Haitian Sun ◽  
Pat Verga ◽  
William W. Cohen
Keyword(s):  
Neural Networks ◽  
Query Language ◽  
Feedforward Neural Networks ◽  
Knowledge Bases ◽  
Language Models ◽  
Computationally Efficient ◽  
Knowledge Based ◽  
Reasoning System ◽  
Symbolic Reasoning ◽  
Computational Intractability

Symbolic reasoning systems based on first-order logics are computationally powerful, and feedforward neural networks are computationally efficient, so unless P=NP, neural networks cannot, in general, emulate symbolic logics. Hence bridging the gap between neural and symbolic methods requires achieving a delicate balance: one needs to incorporate just enough of symbolic reasoning to be useful for a task, but not so much as to cause computational intractability. In this chapter we first present results that make this claim precise, and then use these formal results to inform the choice of a neuro-symbolic knowledge-based reasoning system, based on a set-based dataflow query language. We then present experimental results with a number of variants of this neuro-symbolic reasoner, and also show that this neuro-symbolic reasoner can be closely integrated into modern neural language models.

scholarly journals RULE RESPONDER: RULE-BASED AGENTS FOR THE SEMANTIC-PRAGMATIC WEB

2011 ◽  
Vol 20 (06) ◽  
pp. 1043-1081 ◽  
Author(s):  
ADRIAN PASCHKE ◽  
HAROLD BOLEY
Keyword(s):  
Distributed System ◽  
Autonomous Agents ◽  
Knowledge Bases ◽  
Virtual Organizations ◽  
Rule Based ◽  
Semantic Domain ◽  
Multi Agent ◽  
Web Infrastructure ◽  
Organizational Semiotics ◽  
Event Based

Rule Responder is a Pragmatic Web infrastructure for distributed rule-based event processing multi-agent eco-systems. This allows specifying virtual organizations — with their shared and individual (semantic and pragmatic) contexts, decisions, and actions/events for rule-based collaboration between the distributed members. The (semi-)autonomous agents use rule engines and Semantic Web rules to describe and execute derivation and reaction logic which declaratively implements the organizational semiotics and the different distributed system/agent topologies with their negotiation/coordination mechanisms. They employ ontologies in their knowledge bases to represent semantic domain vocabularies, normative pragmatics and pragmatic context of event-based conversations and actions.

scholarly journals Development strategy formation of the Committee on Artificial Intelligence in the Scientific and Educational Center "Engineering of the Future"

2021 ◽  
Vol 11 (3) ◽  
pp. 260-293
Author(s):  
I.I. Barinov ◽  
◽  
N.M. Borgest ◽  
S.Y. Borovik ◽  
O.N. Granichin ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Development Strategy ◽  
Knowledge Bases ◽  
Enterprise Management ◽  
High Tech ◽  
Digital Twins ◽  
Management Processes ◽  
The Future ◽  
Multi Agent ◽  
A Chain

The Scientific and Educational Center "Engineering of the Future", created on the basis of the Institute of Regional Development of the Samara Region, has formed a number of important sectoral and subject committees, in which it is planned to develop breakthrough technologies in high-tech industries. The Committee on Artificial Intelligence, organized within the framework of the SEC "Engineering of the Future", forms its development strategy. The article outlines the vision for the prospects of such a strategy of the project team, consisting of specialists from universities, academia, design organizations, commercial companies and startups. The key in the proposed strategy is emergent artificial intelligence - it is a spontaneously arising, under the influence of external events or from internal causes or motives, a chain of coordinated state changes by agents who find a solution to a new problem or increase the value of an existing solution. The authors believe that the construction of emergent artificial intelligence is based on multi-agent technologies and ontologies of subject areas. The article formulates the main tasks of the Committee for the coming years and presents a technological project. The project includes the creation of mass production of intelligent resource management systems, personalized by creating digital twins of enterprise management processes, knowledge bases and multi-agent technologies. The essence of the proposed project, reflecting the important priorities of industrial partners, is to create a line of intelligent products and services for all stages of the life cycle of complex high-tech products and build a "factory" of such systems in the form of an open instrumental platform that will allow these enterprises to reduce dependence on the solution provider and on their own develop and modernize such systems. The principles of the Committee's work were proposed, its first potential projects and planned cooperation on these projects to achieve the first practical results were considered.

Constraint mechanisms for knowledge acquisition from computer-aided design data

1993 ◽  
Vol 7 (3) ◽  
pp. 181-188
Author(s):  
Harley R. Myler ◽  
Avelino J. Gonzalez ◽  
Massood Towhidnejad
Keyword(s):  
Process Control ◽  
Knowledge Acquisition ◽  
Computer Aided Design ◽  
Structural Information ◽  
Knowledge Bases ◽  
Knowledge Generation ◽  
Model Based ◽  
Reasoning System ◽  
Computer Aided ◽  
Aided Design

A number of automated reasoning systems find their basis in process control engineering. These programs are often model-based and use individual frames to represent component functionality. This representation scheme allows the process system to be dynamically monitored and controlled as the reasoning system need only simulate the behavior of the modeled system while comparing its behavior to real-time data. The knowledge acquisition task required for the construction of knowledge bases for these systems is formidable because of the necessity of accurately modeling hundreds of physical devices. We discuss a novel approach to the capture of this component knowledge entitled automated knowledge generation (AKG) that utilizes constraint mechanisms predicated on physical behavior of devices for the propagation of truth through the component model base. A basic objective has been to construct a complete knowledge base for a model-based reasoning system from information that resides in computer-aided design (CAD) databases. If CAD has been used in the design of a process control system, then structural information relating the components will be available and can be utilized for the knowledge acquisition function. Relaxation labeling is the constraint-satisfaction method used to resolve the functionality of the network of components. It is shown that the relaxation algorithm used is superior to simple translation schemes.

A Step-By-Step Implementation of a Multi-Agent Currency Trading System

2011 ◽  
pp. 213-253
Author(s):  
Rui Pedro Barbosa ◽  
Orlando Belo
Keyword(s):  
Intelligent Agents ◽  
Trading Strategies ◽  
Case Based Reasoning ◽  
Reasoning System ◽  
Out Of Sample ◽  
Multi Agent ◽  
Trading Agent ◽  
Classification And Regression ◽  
Trading Simulation ◽  
Case Based

With this chapter the authors intend to demonstrate the potential practical use of intelligent agents as autonomous financial traders. The authors propose an architecture to be utilized in the creation of this type of agents, consisting of an ensemble of classification and regression models, a case-based reasoning system and an expert system. This architecture was used to implement six intelligent agents, each being responsible for trading one of the following currency pairs with a 6-hour timeframe: CHF/JPY, EUR/CHF, EUR/JPY, EUR/USD, USD/CHF and USD/JPY. These agents simulated trades during an out-of-sample period going from February of 2007 till July of 2010, having all achieved an acceptable performance. However, their strategies resulted in relatively high drawdowns, and much of their profit disappeared once the trading costs were factored into the trading simulation. In order to overcome these problems, they integrated the agents in a multi-agent system, in which agents communicate their decisions to each other before sending the market orders, and work together to eliminate redundant trades. This system averaged out the returns of the agents, thus eliminating much of the risk associated with their individual trading strategies, and also originated considerable savings in trading expenses. Their results seem to vindicate the usefulness of the proposed trading agent architecture, and also demonstrate that there is indeed a place for intelligent agents in financial markets.

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