scholarly journals Spatial Awareness of a Bacterial Swarm

2018 ◽  
Author(s):  
Harshitha S. Kotian ◽  
Shalini Harkar ◽  
Shubham Joge ◽  
Ayushi Mishra ◽  
Amith Zafal ◽  
...  
Keyword(s):  
Spatial Awareness ◽  
Individual Behaviour ◽  
Complex Behaviour ◽  
Agent Based ◽  
Signalling Molecule ◽  
Two Component ◽  
Bacterial Swarming ◽  
Independent Individual ◽  
Molecule Concentration ◽  
Explicit Communication

AbstractBacteria are perhaps the simplest living systems capable of complex behaviour involving sensing and coherent, collective behaviour an example of which is the phenomena of swarming on agar surfaces. Two fundamental questions in bacterial swarming is how the information gathered by individual members of the swarm is shared across the swarm leading to coordinated swarm behaviour and what specific advantages does membership of the swarm provide its members in learning about their environment. In this article, we show a remarkable example of the collective advantage of a bacterial swarm which enables it to sense inert obstacles along its path. Agent based computational model of swarming revealed that independent individual behaviour in response to a two-component signalling mechanism could produce such behaviour. This is striking because independent individual behaviour without any explicit communication between agents was found to be sufficient for the swarm to effectively compute the gradient of signalling molecule concentration across the swarm and respond to it.

scholarly journals Replacing Rules by Neural Networks A Framework for Agent-Based Modelling

2019 ◽  
Vol 3 (4) ◽  
pp. 51 ◽  
Author(s):  
Georg Jäger
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Decision Process ◽  
Complex Behaviour ◽  
Agent Based ◽  
Agent Based Modelling ◽  
Simple Rules ◽  
Decision Making Processes ◽  
Successful Technique ◽  
Proof Of Principle

Agent-based modelling is a successful technique in many different fields of science. As a bottom-up method, it is able to simulate complex behaviour based on simple rules and show results at both micro and macro scales. However, developing agent-based models is not always straightforward. The most difficult step is defining the rules for the agent behaviour, since one often has to rely on many simplifications and assumptions in order to describe the complicated decision making processes. In this paper, we investigate the idea of building a framework for agent-based modelling that relies on an artificial neural network to depict the decision process of the agents. As a proof of principle, we use this framework to reproduce Schelling’s segregation model. We show that it is possible to use the presented framework to derive an agent-based model without the need of manually defining rules for agent behaviour. Beyond reproducing Schelling’s model, we show expansions that are possible due to the framework, such as training the agents in a different environment, which leads to different agent behaviour.

scholarly journals Möglichkeiten und Grenzen der Analyse von Holzmärkten

2012 ◽  
Vol 163 (10) ◽  
pp. 396-400 ◽  
Author(s):  
Roland Olschewski ◽  
Oliver Thees
Keyword(s):  
State Of The Art ◽  
Agent Based Modeling ◽  
Behavioural Economics ◽  
Future Market ◽  
Individual Behaviour ◽  
Agent Based ◽  
Market Participants ◽  
Real Market ◽  
The Individual ◽  
Forest Economic

Chances and limits of the analysis of wood markets Recent approaches of behavioural economics and agent-based modeling can enhance knowledge about market processes and results and widen the focus for the assessment of future market developments by emphasising the individual behaviour of market participants and scenario techniques. In this article we resume possible contributions of the particular approaches to better describe, explain and forecast real market developments. The exposition is based on state-of-the-art knowledge and reflects insights gained during the 8th Forest Economic Seminar in autumn 2011, where researchers and practitioners presented their findings.

scholarly journals On the Testability of BDI Agent Systems

2014 ◽  
Vol 51 ◽  
pp. 71-131 ◽  
Author(s):  
M. Winikoff ◽  
S. Cranefield
Keyword(s):  
Adaptive Systems ◽  
Software System ◽  
Bdi Agents ◽  
Complex Behaviour ◽  
Agent Based ◽  
Agent Systems ◽  
Failure Handling ◽  
Bdi Agent

Before deploying a software system we need to assure ourselves (and stakeholders) that the system will behave correctly. This assurance is usually done by testing the system. However, it is intuitively obvious that adaptive systems, including agent-based systems, can exhibit complex behaviour, and are thus harder to test. In this paper we examine this "obvious intuition" in the case of Belief-Desire-Intention (BDI) agents. We analyse the size of the behaviour space of BDI agents and show that although the intuition is correct, the factors that influence the size are not what we expected them to be. Specifically, we found that the introduction of failure handling had a much larger effect on the size of the behaviour space than we expected. We also discuss the implications of these findings on the testability of BDI agents.

scholarly journals Principles and State of the Art of Agent-Based Migration Modelling

2021 ◽  
pp. 33-49
Author(s):  
Martin Hinsch ◽  
Jakub Bijak
Keyword(s):  
Complex Systems ◽  
Analytical Approach ◽  
State Of The Art ◽  
Social Systems ◽  
Simulation Modelling ◽  
Migration Route ◽  
Individual Behaviour ◽  
Agent Based ◽  
Practical Advice ◽  
Current State

AbstractMigration as an individual behaviour as well as a macro-level phenomenon happens as part of hugely complex social systems. Understanding migration and its consequences therefore necessitates adopting a careful analytical approach using appropriate tools, such as agent-based models. Still, any model can only be specific to the question it attempts to answer. This chapter provides a general discussion of the key tenets related to modelling complex systems, followed by a review of the current state of the art in the simulation modelling of migration. The subsequent focus of the discussion on the key principles for modelling migration processes, and the context in which they occur, allows for identifying the main knowledge gaps in the existing approaches and for providing practical advice for modellers. In this chapter, we also introduce a model of migration route formation, which is subsequently used as a running example throughout this book.

A two-component active targeting theranostic agent based on graphene quantum dots

2015 ◽  
Vol 3 (17) ◽  
pp. 3583-3590 ◽  
Author(s):  
Xiaojuan Wang ◽  
Xing Sun ◽  
Hua He ◽  
Hao Yang ◽  
Jun Lao ◽  
...  
Keyword(s):  
Quantum Dots ◽  
Tumour Cell ◽  
Cell Imaging ◽  
Graphene Quantum Dots ◽  
Cancer Therapeutics ◽  
Active Targeting ◽  
Agent Based ◽  
Theranostic Agent ◽  
Anti Cancer ◽  
Two Component

Selective tumour cell imaging and synergistic anti-cancer therapeutics are achieved by using the conjugate of AS1411 and graphene quantum dots.

scholarly journals Influenza epidemic model with dynamic social networks of agents with individual behaviour: A self organize perspective

2020 ◽  
Author(s):  
Leonardo Lopez ◽  
Maximiliano Fernandez ◽  
Leonardo Giovanini
Keyword(s):  
Infectious Disease ◽  
Daily Life ◽  
Behavioral Responses ◽  
Behavioral Changes ◽  
Temporal Networks ◽  
Influenza Epidemic ◽  
Individual Behaviour ◽  
Agent Based ◽  
Dynamic Social Networks ◽  
External Stimuli

It's well known the existence of an interplay between the spread of an infectious disease like influenza and behavioral changes of individuals. An outbreak can trigger behavioral responses, at the group and individual levels, which in turn can influence the course of the epidemic. Daily life interactions can be modeled by adaptive temporal networks in an explicit contact space through an agent-based model, where each agent represents the interacting individuals. In this paper we introduce an individual-based model where the behavior of each individual is determined both by the external stimuli and its own appreciation of the environment and can be built as a combination of three interacting blocks: i) individual behavior, ii) social behavior and iii) epidemic state or epidemiological behavior. We fit the model for a real influenza epidemic and perform the model validation, comparing the results with the classical approaches.

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