Creating Intelligent Agents: Combining Agent-Based Modeling with Machine Learning

2021 ◽  
pp. 31-58
Author(s):  
Dale K. Brearcliffe ◽  
Andrew Crooks
Keyword(s):  
Machine Learning ◽  
Intelligent Agents ◽  
Agent Based Modeling ◽  
Agent Based

Drivers of Late Pleistocene human survival and dispersal: an agent-based modeling and machine learning approach

2019 ◽  
Vol 221 ◽  
pp. 105867 ◽  
Author(s):  
Ali R. Vahdati ◽  
John David Weissmann ◽  
Axel Timmermann ◽  
Marcia S. Ponce de León ◽  
Christoph P.E. Zollikofer
Keyword(s):  
Machine Learning ◽  
Late Pleistocene ◽  
Agent Based Modeling ◽  
Learning Approach ◽  
Agent Based ◽  
Human Survival ◽  
Machine Learning Approach

scholarly journals Topology Applied to Machine Learning: From Global to Local

2021 ◽  
Vol 4 ◽  
Author(s):  
Henry Adams ◽  
Michael Moy
Keyword(s):  
Machine Learning ◽  
Materials Science ◽  
Klein Bottle ◽  
Persistent Homology ◽  
Agent Based Modeling ◽  
Learning Problems ◽  
Local Geometry ◽  
Agent Based ◽  
Learning Tasks ◽  
Circle Model

Through the use of examples, we explain one way in which applied topology has evolved since the birth of persistent homology in the early 2000s. The first applications of topology to data emphasized the global shape of a dataset, such as the three-circle model for 3 × 3 pixel patches from natural images, or the configuration space of the cyclo-octane molecule, which is a sphere with a Klein bottle attached via two circles of singularity. In these studies of global shape, short persistent homology bars are disregarded as sampling noise. More recently, however, persistent homology has been used to address questions about the local geometry of data. For instance, how can local geometry be vectorized for use in machine learning problems? Persistent homology and its vectorization methods, including persistence landscapes and persistence images, provide popular techniques for incorporating both local geometry and global topology into machine learning. Our meta-hypothesis is that the short bars are as important as the long bars for many machine learning tasks. In defense of this claim, we survey applications of persistent homology to shape recognition, agent-based modeling, materials science, archaeology, and biology. Additionally, we survey work connecting persistent homology to geometric features of spaces, including curvature and fractal dimension, and various methods that have been used to incorporate persistent homology into machine learning.

scholarly journals Heterogeneity: method and applications for complex systems analysis

2022 ◽  
Vol 2159 (1) ◽  
pp. 012013
Author(s):  
J M Redondo ◽  
J S Garcia ◽  
C Bustamante-Zamudio ◽  
M F Pereira ◽  
H F Trujillo
Keyword(s):  
Machine Learning ◽  
Complex Systems ◽  
Research And Development ◽  
Systems Analysis ◽  
Ecological Systems ◽  
Agent Based Modeling ◽  
Time Lags ◽  
Agent Based ◽  
Physical Systems ◽  
Sustainability Analysis

Abstract Socio-ecological systems like another physical systems are complex systems in which are required methods for analyzes their non-linearities, thresholds, feedbacks, time lags, and resilience. This involves understanding the heterogeneity of the interactions in time and space. In this article, we carry out the proposition and demonstration of two methods that allow the calculation of heterogeneity in different contexts. The practical effectiveness of the methods is presented through applications in sustainability analysis, land transport, and governance. It is concluded that the proposed methods can be used in various research and development areas due to their ease of being considered in broad modeling frameworks as agent-based modeling, system dynamics, or machine learning, although it could also be used to obtain point measurements only by replacing values.

Agent-Based Intelligent System Modeling

2011 ◽  
pp. 51-57
Author(s):  
Zaiyong Tang ◽  
Xiaoyu Huang ◽  
Kallol Bagchi
Keyword(s):  
Complex Systems ◽  
System Dynamics ◽  
Intelligent Agents ◽  
Intelligent Systems ◽  
Intelligent System ◽  
System Modeling ◽  
Agent Based Modeling ◽  
Changing Environment ◽  
Agent Based ◽  
Sense And Respond

An intelligent system is a system that has, similar to a living organism, a coherent set of components and subsystems working together to engage in goal-driven activities. In general, an intelligent system is able to sense and respond to the changing environment; gather and store information in its memory; learn from earlier experiences; adapt its behaviors to meet new challenges; and achieve its pre-determined or evolving objectives. The system may start with a set of predefined stimulusresponse rules. Those rules may be revised and improved through learning. Anytime the system encounters a situation, it evaluates and selects the most appropriate rules from its memory to act upon. Most human organizations such as nations, governments, universities, and business firms, can be considered as intelligent systems. In recent years, researchers have developed frameworks for building organizations around intelligence, as opposed to traditional approaches that focus on products, processes, or functions (e.g., Liang, 2002; Gupta and Sharma, 2004). Today’s organizations must go beyond traditional goals of efficiency and effectiveness; they need to have organizational intelligence in order to adapt and survive in a continuously changing environment (Liebowitz, 1999). The intelligent behaviors of those organizations include monitoring of operations, listening and responding to stakeholders, watching the markets, gathering and analyzing data, creating and disseminating knowledge, learning, and effective decision making. Modeling intelligent systems has been a challenge for researchers. Intelligent systems, in particular, those involve multiple intelligent players, are complex systems where system dynamics does not follow clearly defined rules. Traditional system dynamics approaches or statistical modeling approaches rely on rather restrictive assumptions such as homogeneity of individuals in the system. Many complex systems have components or units which are also complex systems. This fact has significantly increased the difficulty of modeling intelligent systems. Agent-based modeling of complex systems such as ecological systems, stock market, and disaster recovery has recently garnered significant research interest from a wide spectrum of fields from politics, economics, sociology, mathematics, computer science, management, to information systems. Agent-based modeling is well suited for intelligent systems research as it offers a platform to study systems behavior based on individual actions and interactions. In the following, we present the concepts and illustrate how intelligent agents can be used in modeling intelligent systems. We start with basic concepts of intelligent agents. Then we define agent-based modeling (ABM) and discuss strengths and weaknesses of ABM. The next section applies ABM to intelligent system modeling. We use an example of technology diffusion for illustration. Research issues and directions are discussed next, followed by conclusions.

A Prototype Agent Based Model and Machine Learning Hybrid System for Healthcare Decision Support

2011 ◽  
Vol 2 (4) ◽  
pp. 67-90 ◽  
Author(s):  
Marek Laskowski
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Patient Flow ◽  
Policy Decision ◽  
Healthcare Policy ◽  
Agent Based Modeling ◽  
Learning System ◽  
Agent Based Model ◽  
Agent Based ◽  
Markov Decision

Science is on the verge of practical agent based modeling decision support systems capable of machine learning for healthcare policy decision support. The details of integrating an agent based model of a hospital emergency department with a genetic programming machine learning system are presented in this paper. A novel GP heuristic or extension is introduced to better represent the Markov Decision Process that underlies agent decision making in an unknown environment. The capabilities of the resulting prototype for automated hypothesis generation within the context of healthcare policy decision support are demonstrated by automatically generating patient flow and infection spread prevention policies. Finally, some observations are made regarding moving forward from the prototype stage.

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