Artificial Societies of Intelligent Agents

Gershenson, Carlos

doi:10.2139/ssrn.371641

Cited by 10 publications

(6 citation statements)

References 69 publications

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“…Agents have been used to model cognitive systems of different flavors, including rational (Wooldridge and Jennings, 1995 ), adaptive (Maes, 1994 ), social (Epstein and Axtell, 1996 ; Gershenson, 2001 ), and economic (Arthur, 1999 ; Challet et al, 2013 ).…”

Section: Approachesmentioning

confidence: 99%

Guiding the Self-Organization of Cyber-Physical Systems

Gershenson

2020

Front. Robot. AI

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Self-organization offers a promising approach for designing adaptive systems. Given the inherent complexity of most cyber-physical systems, adaptivity is desired, as predictability is limited. Here I summarize different concepts and approaches that can facilitate self-organization in cyber-physical systems, and thus be exploited for design. Then I mention real-world examples of systems where self-organization has managed to provide solutions that outperform classical approaches, in particular related to urban mobility. Finally, I identify when a centralized, distributed, or self-organizing control is more appropriate.

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Section: Approachesmentioning

confidence: 99%

Guiding the Self-Organization of Cyber-Physical Systems

Gershenson

2020

Front. Robot. AI

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“…Societies are defined by the interactions of individuals of the same species. The computational modeling of social systems has become very popular because it enables the systematic exploration of possibilities of social interaction, which are very difficult to achieve with complex societies (Gilbert and Conte, 1995;Epstein Axtell, 1996;Gershenson, 2001;Epstein, 2006).…”

Section: Artificial Societiesmentioning

confidence: 99%

The Past, Present, and Future of Artificial Life

et al. 2014

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For millennia people have wondered what makes the living different from the non-living. Beginning in the mid-1980s, artificial life has studied living systems using a synthetic approach: build life in order to understand it better, be it by means of software, hardware, or wetware. This review provides a summary of the advances that led to the development of artificial life, its current research topics, and open problems and opportunities. We classify artificial life research into 14 themes: origins of life, autonomy, self-organization, adaptation (including evolution, development, and learning), ecology, artificial societies, behavior, computational biology, artificial chemistries, information, living technology, art, and philosophy. Being interdisciplinary, artificial life seems to be losing its boundaries and merging with other fields.

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“…Psychologists have developed a range of motivation theories which describe different aspects of these roles. There are also many different computational models of motivation, ranging from action selection architectures inspired by biological systems [5,11] through to motivated goal creation schemes based on cognitive theories of the mind [1,20,24,29,31]. These models have been used to focuses an agent's reasoning and action around a particular subset of its perception, to generate goals or to trigger other processes that satisfy or stimulate its motivational mechanism.…”

Section: A Computational Schema For Intrinsic Motivationmentioning

confidence: 99%

Intrinsically Motivated Intelligent Sensed Environments

Maher

Kasmarik

Macindoe

2006

Lecture Notes in Computer Science

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Abstract. Intelligent rooms comprise hardware devices that support human activities in a room and software that has some level of control over the devices. "Intelligent" implies that the room is considered to behave in an intelligent manner or includes some aspect of artificial intelligence in its implementation. The focus of this paper is intelligent sensed environments, including rooms or interactive spaces that display adaptive behaviour through learning and motivation. We present motivated agent models that incorporate machine learning in which the motivation component eliminates the need for a benevolent teacher to prepare problem specific reward functions or training examples. Our model of motivation is based on concepts of "curiosity", "novelty" and "interest". We explore the potential for this model through the implementation of a curious place.

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Artificial Societies of Intelligent Agents

Cited by 10 publications

References 69 publications

Guiding the Self-Organization of Cyber-Physical Systems

Guiding the Self-Organization of Cyber-Physical Systems

The Past, Present, and Future of Artificial Life

Intrinsically Motivated Intelligent Sensed Environments

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