Rough Ethology: Towards a Biologically-Inspired Study of Collective Behavior in Intelligent Systems with Approximation Spaces

2005 ◽  
pp. 153-174 ◽  
Author(s):  
James F. Peters
Keyword(s):  
Collective Behavior ◽  
Intelligent Systems ◽  
Biologically Inspired ◽  
Approximation Spaces

Influence of Leaders on Mean Square Consentability in Biologically-Inspired Stochastic Networks

2011 ◽  
Author(s):  
Nicole Abaid ◽  
Maurizio Porfiri
Keyword(s):  
Collective Behavior ◽  
Information Exchange ◽  
Stochastic Networks ◽  
Closed Form Expression ◽  
Convergence Factor ◽  
Form Expression ◽  
Consensus Protocol ◽  
Biologically Inspired ◽  
Size Number ◽  
Consensus Dynamics

In this work, we study a discrete-time consensus protocol for a group of agents which communicate over a class of stochastically switching networks inspired by fish schooling. The network model incorporates the phenomenon of numerosity that has a prominent role on the collective behavior of animal groups by defining the individuals’ perception of numbers. The agents comprise leaders, which share a common state, and followers, which update their states based on information exchange among neighboring agents. We write a closed form expression for the asymptotic convergence factor of the protocol, which measures the decay rate of disagreement among the followers’ and the leaders’ states. Numerical simulations are conducted to validate analytical results and illustrate the consensus dynamics as a function of the group size, number of leaders in the group, and the numerosity.

Biologically-Inspired Learning and Intelligence

2011 ◽  
pp. 184-215
Author(s):  
Turgay Temel
Keyword(s):  
Decision Making ◽  
Information Processing ◽  
Intelligent Systems ◽  
Fuzzy Controller ◽  
Fuzzy Reasoning ◽  
Biologically Inspired ◽  
Rule Based ◽  
Trade Offs ◽  
Neural Information ◽  
Neural Information Processing

Since biologically-inspired intelligent systems with learning and decision-making capabilities vastly act upon comparison among inputs, the ability to select those inputs which satisfy certain conditions is of great significance in realization of such systems. Moreover intelligent systems need to operate with concurrency so as to reflect inherited capability of their biological counterparts like human. Due to difficulties in programmability, storage and design complexities, the analog implementation has been considerably less favored in most computational information processing systems. However, in the case of biologically-inspired computation, their suitability for concurrency, accuracy and capability in simulating the natural behavior of biological signals, analog neural information processing is regarded an attractive solution. Benefiting the full advantage involves comprehensive understanding and knowledge of what trade-offs can be established with design topologies available and theoretical necessities. On the other hand, fuzzy reasoning offers rule-based inferential manipulation on inputs where it expresses the input-output relationship in terms of clauses. Considering a nonlinear operation carried out by artificial neural networks based on experience, realization of rule-based clauses is much easier. This chapter introduces fundamental notions of fuzzy reasoning, and fuzzy-based analog design approaches. Rather than resorting on analytical derivation for the architecture of interest, the main focus is directed at suitability for use, which is expected to indicate possibility toward developing complex intelligent systems. It should be noted that the circuits having selectivity property in deciding maximum and/or minimum on inputs demonstrate their use in much broader field than inference, thus they have great importance in realization of information processing systems. The chapter presents a very compact selectivity circuit as decision maker for the minimum of its inputs. Further to it, a considerably simple yet elaborate membership structure is introduced. The circuit simplifies the fuzzy controller design. Since mostly decision making is performed on a (dis)similarity measure between inputs, e.g. the input and label patterns for respective categories, it is convenient to express the proximity in terms of a metric. The chapter also introduces important designs proposed for assessing the similarity in the Euclidean distance.

scholarly journals Emergent Specialization in Biologically Inspired Collective Behavior Systems

2008 ◽  
pp. 215-253 ◽  
Author(s):  
G. S. Nitschke ◽  
M. C. Schut ◽  
A. E. Eiben
Keyword(s):  
Complex Adaptive Systems ◽  
Collective Behavior ◽  
Adaptive Systems ◽  
Future Research ◽  
Problem Solver ◽  
Biologically Inspired ◽  
Design Methodologies ◽  
Complex Adaptive ◽  
Optimal Efficiency ◽  
Behavior Systems

Specialization is observable in many complex adaptive systems and is thought by many to be a fundamental mechanism for achieving optimal efficiency within organizations operating within complex adaptive systems. This chapter presents a survey and critique of collective behavior systems designed using biologically inspired principles. Specifically, we are interested in collective behavior systems where specialization emerges as a result of system dynamics and where emergent specialization is used as a problem solver or means to increase task performance. The chapter presents an argument for developing design methodologies and principles that facilitate emergent specialization in collective behavior systems. Open problems of current research as well as future research directions are highlighted for the purpose of encouraging the development of such emergent specialization design methodologies.

Biomimetics and the Evolution of Robotics and Intelligent Systems

2018 ◽  
pp. 1-25
Author(s):  
Maki K. Habib ◽  
Fusaomi Nagata
Keyword(s):  
Intelligent Systems ◽  
Research Field ◽  
Biologically Inspired ◽  
Intelligent Machines ◽  
Interdisciplinary Field ◽  
Research Activities ◽  
Engineering Sciences ◽  
Application Potential ◽  
Design Ideas ◽  
Scientific Ideas

Biologically inspired systems, known as “biomimetics” or the “mimicry of nature,” is an interdisciplinary scientific research field inspired by nature and featured by the technology outcome (hardware and software) and lies at the interface of biology, physics, chemistry, information, and engineering sciences. Biomimetics is initiated by making nature a model of inspiration that would immensely help conscious abstraction of new innovative principles and creative design ideas and concepts that help developing new techniques and functionalities, seeking new paradigms and methods, designing new materials, and developing new streams of intelligent machines, robots, systems, devices, algorithms, etc. Biologically inspired approaches create a new reality with great development and application potential with the goal of identifying specific desirable qualities and attributes in biological systems and using them in the design of new products and systems. This chapter provides the importance of biomimetic as an interdisciplinary field and its evolution, advances, challenges, and constraints along with the associated enabling technologies supporting its growth. In addition, it introduces scientific ideas and directions of research activities in the field. The chapter also presents key developments in the field of biomimetic robots and underlines the challenges facing it.

Cognitive Process of Moral Decision-Making for Autonomous Agents

2013 ◽  
Vol 5 (4) ◽  
pp. 61-76 ◽  
Author(s):  
José-Antonio Cervantes ◽  
Luis-Felipe Rodríguez ◽  
Sonia López ◽  
Félix Ramos ◽  
Francisco Robles
Keyword(s):  
Decision Making ◽  
Intelligent Systems ◽  
Computational Models ◽  
Autonomous Agents ◽  
Empirical Studies ◽  
Theoretical Models ◽  
Moral Decision ◽  
Moral Decision Making ◽  
Biologically Inspired ◽  
Human Decision

There are a great variety of theoretical models of cognition whose main purpose is to explain the inner workings of the human brain. Researchers from areas such as neuroscience, psychology, and physiology have proposed these models. Nevertheless, most of these models are based on empirical studies and on experiments with humans, primates, and rodents. In fields such as cognitive informatics and artificial intelligence, these cognitive models may be translated into computational implementations and incorporated into the architectures of intelligent autonomous agents (AAs). Thus, the main assumption in this work is that knowledge in those fields can be used as a design approach contributing to the development of intelligent systems capable of displaying very believable and human-like behaviors. Decision-Making (DM) is one of the most investigated and computationally implemented functions. The literature reports several computational models that enable AAs to make decisions that help achieve their personal goals and needs. However, most models disregard crucial aspects of human decision-making such as other agents' needs, ethical values, and social norms. In this paper, the authors present a set of criteria and mechanisms proposed to develop a biologically inspired computational model of Moral Decision-Making (MDM). To achieve a process of moral decision-making believable, the authors propose a cognitive function to determine the importance of each criterion based on the mood and emotional state of AAs, the main objective the model is to enable AAs to make decisions based on ethical and moral judgment.

scholarly journals Informational architecture across non-living and living collectives

2021 ◽  
Author(s):  
Hyunju Kim ◽  
Gabriele Valentini ◽  
Jake Hanson ◽  
Sara Imari Walker
Keyword(s):  
Collective Behavior ◽  
Intelligent Systems ◽  
Degrees Of Freedom ◽  
Physical Models ◽  
Information Architecture ◽  
Biological Phenomenon ◽  
Future Directions ◽  
Collective Behaviors ◽  
Information Theoretic ◽  
Physical Systems

AbstractCollective behavior is widely regarded as a hallmark property of living and intelligent systems. Yet, many examples are known of simple physical systems that are not alive, which nonetheless display collective behavior too, prompting simple physical models to often be adopted to explain living collective behaviors. To understand collective behavior as it occurs in living examples, it is important to determine whether or not there exist fundamental differences in how non-living and living systems act collectively, as well as the limits of the intuition that can be built from simpler, physical examples in explaining biological phenomenon. Here, we propose a framework for comparing non-living and living collectives as a continuum based on their information architecture: that is, how information is stored and processed across different degrees of freedom. We review diverse examples of collective phenomena, characterized from an information-theoretic perspective, and offer views on future directions for quantifying living collective behaviors based on their informational structure.

Bats Versus Bugs: Collective Behavior of Prey Decreases Predation in a Biologically-Inspired Multi-Agent System

2013 ◽  
Author(s):  
Yuan Lin ◽  
Nicole Abaid
Keyword(s):  
Collective Behavior ◽  
Dimensional Space ◽  
Three Dimensional ◽  
Biologically Inspired ◽  
Agent Based ◽  
Big Brown Bats ◽  
Hunting Success ◽  
Multi Agent ◽  
The Impact ◽  
Three Dimensional Space

In this paper, we establish an agent-based model to study the impact of collective behavior of a prey species on the hunting success of predators inspired by insectivorous bats and swarming insects, called “bugs”. In the model, we consider bats preying on bugs in a three-dimensional space with periodic boundaries. The bugs follow one of the two regimes: either they swarm randomly without interacting with peers, or they seek to align their velocity directions, which results in collective behavior. Simultaneously, the bats sense their environment with a sensing space inspired by big brown bats (Eptesicus fuscus) and independently prey on bugs. We define order parameters to measure the alignment and cohesion of the bugs and relate these quantities to the cohesion and the hunting success of the bats. Comparing the results when the bugs swarm randomly or collectively, we find that collectively behaving bugs tend to align, which results in relatively more cohesive groups. In addition, cohesion among bats is induced since bats may be attracted to the same localized bug group. Due to the fact that bats need to hunt more widely for groups of bugs, collectively behaving bugs suffer less predation compared to their randomly swarming counterparts. These findings are supported by the biological literature which cites protection from predation as a primary motivator for social behavior.

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