scholarly journals Agent-Based Evolutionary Model for Knowledge Acquisition in Dynamical Environments

2006 ◽  
pp. 839-846 ◽  
Author(s):  
Wojciech Froelich ◽  
Marek Kisiel-Dorohinicki ◽  
Edward Nawarecki
Keyword(s):  
Knowledge Acquisition ◽  
Evolutionary Model ◽  
Agent Based

Agent-Mediated Knowledge Acquisition for User Profiling

2008 ◽  
pp. 1486-1501
Author(s):  
A. Andreevskaia ◽  
R. Abi-Aad ◽  
T. Radhakrishnan
Keyword(s):  
Knowledge Transfer ◽  
Electronic Commerce ◽  
Knowledge Acquisition ◽  
Information Exchange ◽  
Software Agents ◽  
User Profile ◽  
User Model ◽  
User Profiling ◽  
Agent Based

This chapter presents a tool for knowledge acquisition for user profiling in electronic commerce. The knowledge acquisition in e-commerce is a challenging task that requires specific tools in order to facilitate the knowledge transfer from the user to the system. The proposed tool is based on a hierarchical user model and is agent-based. The architecture of the tool incorporates four software agents: processing agent maintaining the user profile, validating agent interacting with the user when information validation is needed, monitoring agent monitoring the effects of the changes made to the user profile, and a filtering agent ensuring the safe information exchange with other software.

Differential Learning Expert System in Data Management

2009 ◽  
pp. 597-604
Author(s):  
R. Manjunath
Keyword(s):  
Expert System ◽  
Expert Systems ◽  
Data Management ◽  
Knowledge Acquisition ◽  
User Interfaces ◽  
High Performance ◽  
Evolutionary Model ◽  
Human Expert ◽  
Incremental Development ◽  
Differential Learning

Expert systems have been applied to many areas of research to handle problems effectively. Designing and implementing an expert system is a difficult job, and it usually takes experimentation and experience to achieve high performance. The important feature of an expert system is that it should be easy to modify. They evolve gradually. This evolutionary or incremental development technique has to be noticed as the dominant methodology in the expert-system area. The simple evolutionary model of an expert system is provided in B. Tomic, J. Jovanovic, & V. Devedzic, 2006. Knowledge acquisition for expert systems poses many problems. Expert systems depend on a human expert to formulate knowledge in symbolic rules. The user can handle the expert systems by updating the rules through user interfaces (J. Jovanovic, D. Gasevic, V. Devedzic, 2004). However, it is almost impossible for an expert to describe knowledge entirely in the form of rules. An expert system may therefore not be able to diagnose a case that the expert is able to. The question is how to extract experience from a set of examples for the use of expert systems.

scholarly journals Intelligent Agents in Co-Evolving Knowledge Networks

Mathematics ◽  
2021 ◽  
Vol 9 (1) ◽  
pp. 103
Author(s):  
Evangelos Ioannidis ◽  
Nikos Varsakelis ◽  
Ioannis Antoniou
Keyword(s):  
Knowledge Acquisition ◽  
Intelligent Agents ◽  
Diffusion Mechanism ◽  
Network Size ◽  
Agent Based ◽  
Agent Interaction ◽  
Selection Decision ◽  
Knowledge Levels ◽  
Innovation Rate ◽  
The Impact

We extend the agent-based models for knowledge diffusion in networks, restricted to random mindless interactions and to “frozen” (static) networks, in order to take into account intelligent agents and network co-evolution. Intelligent agents make decisions under bounded rationality. This is the key distinction of intelligent interacting agents compared to mindless colliding molecules, involved in the usual diffusion mechanism resulting from accidental collisions. The co-evolution of link weights and knowledge levels is modeled at the local microscopic level of “agent-to-agent” interaction. Our network co-evolution model is actually a “learning mechanism”, where weight updates depend on the previous values of both weights and knowledge levels. The goal of our work is to explore the impact of (a) the intelligence of the agents, modeled by the selection-decision rule for knowledge acquisition, (b) the innovation rate of the agents, (c) the number of “top innovators” and (d) the network size. We find that rational intelligent agents transform the network into a “centralized world”, reducing the entropy of their selections-decisions for knowledge acquisition. In addition, we find that the average knowledge, as well as the “knowledge inequality”, grow exponentially.

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