Dynamic Change of a Multi-Agent Workflow for Patent Invention Using a Utility Function

2007 ◽  
Author(s):  
Szu-Yin Lin ◽  
Bo-Yuan Chen ◽  
Hsien-Tzung Wu ◽  
Von-Wun Soo ◽  
C.C. Ku
Keyword(s):  
Utility Function ◽  
Dynamic Change ◽  
Multi Agent

Simulation of Skulduggery in a Multi-Agent System

2015 ◽  
Vol 7 (1) ◽  
pp. 17-31
Author(s):  
Rasoul Ramezanian ◽  
Akram Emdadi
Keyword(s):  
Utility Function ◽  
Test Session ◽  
Testing Session ◽  
Multi Agent System ◽  
Agent System ◽  
Factors Affecting ◽  
Artificial Society ◽  
Control Factors ◽  
Multi Agent ◽  
Level Of Knowledge

In a testing session, students may want to use the information of other students, which is cheating. The authors of this paper develop an artificial society to model and simulate this situation. They consider two control factors to increase the incentive of students to not cheat. The first factor is the penalty for similarity between responses (as much as two answer-sheets of two students are the same, their final grades decrease). The second factor is the observers who look into the students and do not allow the observed students to cheat. In this model, agents participate in a test based on their level of knowledge, location and two above factors, deciding whether or not to cheat. These components are used to formulate the utility function. Taking advantage of the developed artificial society, the authors now study the above factors affecting the amount of cheating in a test session.

scholarly journals Analyzing the Coevolution of Mobile Application Diffusion and Social Network: A Multi-Agent Model

Entropy ◽  
2021 ◽  
Vol 23 (5) ◽  
pp. 521
Author(s):  
Zhenyu Zhang ◽  
Huirong Zhang ◽  
Lixin Zhou ◽  
Yanfeng Li
Keyword(s):  
Social Network ◽  
Mobile Applications ◽  
Mobile Application ◽  
Dynamic Change ◽  
Real Life ◽  
Network Evolution ◽  
Agent Model ◽  
Value Perception ◽  
Multi Agent ◽  
The Impact

The successful diffusion of mobile applications in user groups can establish a good image for enterprises, gain a good reputation, fight for market share, and create commercial profits. Thus, it is of great significance for the successful diffusion of mobile applications to study mobile application diffusion and social network coevolution. Firstly, combined with a social network’s dynamic change characteristics in real life, a mobile application users’ social network evolution mechanism was designed. Then, a multi-agent model of the coevolution of a social network and mobile application innovation diffusion was constructed. Finally, the impact of mobile applications’ value perception revenue, use cost, marketing promotion investment, and the number of seed users on the coevolution of social network and mobile application diffusion were analyzed. The results show that factors such as the network structure, the perceived value income, the cost of use, the marketing promotion investment, and the number of seed users have an important impact on mobile application diffusion.

scholarly journals Speeding Up Incomplete GDL-based Algorithms for Multi-agent Optimization with Dense Local Utilities

2020 ◽  
Author(s):  
Yanchen Deng ◽  
Bo An
Keyword(s):  
Utility Function ◽  
Branch And Bound ◽  
State Of The Art ◽  
Search Space ◽  
Computational Effort ◽  
The State ◽  
Computational Overhead ◽  
Multi Agent

Incomplete GDL-based algorithms including Max-sum and its variants are important methods for multi-agent optimization. However, they face a significant scalability challenge as the computational overhead grows exponentially with respect to the arity of each utility function. Generic Domain Pruning (GDP) technique reduces the computational effort by performing a one-shot pruning to filter out suboptimal entries. Unfortunately, GDP could perform poorly when dealing with dense local utilities and ties which widely exist in many domains. In this paper, we present several novel sorting-based acceleration algorithms by alleviating the effect of densely distributed local utilities. Specifically, instead of one-shot pruning in GDP, we propose to integrate both search and pruning to iteratively reduce the search space. Besides, we cope with the utility ties by organizing the search space of tied utilities into AND/OR trees to enable branch-and-bound. Finally, we propose a discretization mechanism to offer a tradeoff between the reconstruction overhead and the pruning efficiency. We demonstrate the superiorities of our algorithms over the state-of-the-art from both theoretical and experimental perspectives.

Integrated Process Planning and Scheduling Using Multi-Agent Methodology

2016 ◽  
Vol 834 ◽  
pp. 193-198 ◽  
Author(s):  
Jelena Petronijević ◽  
Milica Petrović ◽  
Najdan Vuković ◽  
Marko Mitić ◽  
Bojan Babić ◽  
...  
Keyword(s):  
Decision Making ◽  
Process Planning ◽  
Dynamic Change ◽  
Integrated Process ◽  
Multi Agent Systems ◽  
Planning And Scheduling ◽  
Market Growth ◽  
Decentralized Decision Making ◽  
Multi Agent ◽  
Traditional Manufacturing

Market growth and mass customization cause a need for a change in traditional manufacturing. Decentralized decision making and integration of process planning is necessary in order to become concurrent in the market. The paper presents decentralized decision making methodology using multi-agent systems. The model is used for integrated process planning and scheduling based on the minimum processing time under dynamic change of the environment. Two types of disturbance are used to represent the change: part arrival and machine breakdown. The proposed model comprises part agent, job agent, machine agent and optimization agent. Comparative analysis is conducted using simulation in AnyLogic software in order to verify the proposed approach.

INNOVATION AND SELF-ORGANIZATION IN A MULTI-AGENT MODEL

2009 ◽  
Vol 12 (02) ◽  
pp. 233-253 ◽  
Author(s):  
TANYA ARAÚJO ◽  
R. VILELA MENDES
Keyword(s):  
Utility Function ◽  
The Other ◽  
Self Organization ◽  
Abstract Model ◽  
Agent Based ◽  
Agent Model ◽  
Multi Agent

A model is developed to study the effectiveness of innovation and its impact on structure creation on agent-based societies. The abstract model that is developed is easily adapted to any particular field. In an interacting environment, the agents receive something from the environment (the other agents) in exchange for their effort and pay the environment a certain amount of value for the fulfilling of their needs or for the very price of existence in that environment. This is coded by two bit strings and the dynamics of the exchange is based on the matching of these strings to those of the other agents. Innovation is related to the adaptation by the agents of their bit strings to improve some utility function.

A Multiattribute Utility Approach to Target Assignment

2009 ◽  
Author(s):  
Xi Chen ◽  
Ali E. Abbas ◽  
Dusˇan M. Stipanovic´
Keyword(s):  
Utility Function ◽  
Kinematic Model ◽  
Multiattribute Utility ◽  
Control Lyapunov Function ◽  
Control Laws ◽  
Function Design ◽  
Target Assignment ◽  
Conduct Sensitivity Analysis ◽  
Multi Agent ◽  
Utility Approach

This paper introduces the multiattribute utility theory to the control Lyapunov function design framework. As an illustration we focus on the problem of multi-target assignment. With this formulation, we use a global multiattribute utility function as a multivariate objective function that should be minimized for the agents to achieve their objectives. The objectives represent deviations of each agent from specified targets. We provide closed form feedback control laws, based on the multiattribute utility function, for general nonlinear multi-agent system models affine in control. Finally, we present simulation results and conduct sensitivity analysis for two different models that are affine in control; basic kinematic model and nonlinear nonholonomic unicycle model.

scholarly journals Intelligent Exploration Approaches Based on Utility Functions Optimization for Multi-Agent Environment Applications

2021 ◽  
Vol 11 (5) ◽  
pp. 2408
Author(s):  
José Oñate-López ◽  
Loraine Navarro ◽  
Christian G. Quintero M. ◽  
Mauricio Pardo
Keyword(s):  
Neural Networks ◽  
Genetic Algorithms ◽  
Path Planning ◽  
Utility Function ◽  
Indoor Environment ◽  
Planning Problem ◽  
Multi Agent ◽  
The Individual ◽  
Intelligent Approach ◽  
Path Planning Problem

In this work, the problem of exploring an unknown environment with a team of agents and search different targets on it is considered. The key problem to be solved in multiple agents is choosing appropriate target points for the individual agents to simultaneously explore different regions of the environment. An intelligent approach is presented to coordinate several agents using a market-based model to identify the appropriate task for each agent. It is proposed to compare the fitting of the market utility function using neural networks and optimize this function using genetic algorithms to avoid heavy computation in the Non-Polynomial (NP: nondeterministic polynomial time) path-planning problem. An indoor environment inspires the proposed approach with homogeneous physical agents, and its performance is tested in simulations. The results show that the proposed approach allocates agents effectively to the environment and enables them to carry out their mission quickly.

Sign in / Sign up

Export Citation Format

Share Document