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Bibliografias temáticas / Multi-agent interaction / Livros

Livros sobre o tema "Multi-agent interaction"

Siga este link para ver outros tipos de publicações sobre o tema: Multi-agent interaction.

Autor: Grafiati

Publicado: 25 de maio de 2024

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1

1960-, Sun Ron, ed. Cognition and multi-agent interaction: From cognitive modeling to social simulation. Cambridge: Cambridge University Press, 2006.

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2

Khosla, Rajiv. Intelligent Multimedia Multi-Agent Systems: A Human-Centered Approach. Boston, MA: Springer US, 2000.

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3

Wayne, Wobcke, Sen Sandip, Sugawara Toshiharu e SpringerLink (Online service), eds. PRIMA 2012: Principles and Practice of Multi-Agent Systems: 15th International Conference, Kuching, Sarawak, Malaysia, September 3-7, 2012. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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4

Iyad, Rahwan, Parsons Simon e SpringerLink (Online service), eds. Argumentation in Multi-Agent Systems: 7th International Workshop, ArgMAS 2010 Toronto, ON, Canada, May 10, 2010 Revised, Selected and Invited Papers. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011.

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5

International Conference on Multi-Agent Systems (1st 1995 San Francisco, Calif.). ICMAS--95, First International Conference on Multi-Agent Systems: Proceedings, June 12-14, 1995, San Francisco, California. Menlo Park, Calif: AAAI Press, 1995.

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6

Sun, Ron, ed. Cognition and Multi-Agent Interaction. Cambridge University Press, 2005. http://dx.doi.org/10.1017/cbo9780511610721.

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7

W, Brockett Roger, Tarokh Vahid e Lu Yue, eds. Multi-Agent Systems with Reciprocal Interaction Laws. 2014.

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8

Sun, Ron. Cognition and Multi-agent Interaction: From Cognitive Modeling to Social Simulation. Cambridge University Press, 2006.

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9

Sun, Ron. Cognition and Multi-Agent Interaction: From Cognitive Modeling to Social Simulation. Cambridge University Press, 2008.

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10

Sun, Ron. Cognition and Multi-Agent Interaction: From Cognitive Modeling to Social Simulation. Cambridge University Press, 2005.

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11

Sun, Ron. Cognition and Multi-Agent Interaction: From Cognitive Modeling to Social Simulation. Cambridge University Press, 2009.

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12

Sun, Ron. Cognition and Multi-Agent Interaction: From Cognitive Modeling to Social Simulation. Cambridge University Press, 2005.

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13

Sun, Ron. Cognition and Multi-Agent Interaction: From Cognitive Modeling to Social Simulation. Cambridge University Press, 2006.

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14

Intelligent Multimedia Multi-Agent Systems: A Human-Centered Approach (The Springer International Series in Engineering and Computer Science). Springer, 2000.

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15

Green, Nancy, Matteo Baldoni, Cristina Baroglio, Floris Bex e Floriana Grasso. Principles and Practice of Multi-Agent Systems : International Workshops: IWEC 2014, Gold Coast, QLD, Australia, December 1-5, 2014, and CMNA XV and IWEC 2015, Bertinoro, Italy, October 26, 2015, Revised Selected Papers. Springer, 2017.

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16

Principles and Practice of Multi-Agent Systems: 13th International Conference, PRIMA 2010, Kolkata, India, November 12-15, 2010, Revised Selected Papers. Springer, 2011.

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17

Liu, Alan, Nirmit Desai e Michael Winikoff. Principles and Practice of Multi-Agent Systems: 13th International Conference, PRIMA 2010, Kolkata, India, November 12-15, 2010, Revised Selected Papers. Springer, 2012.

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18

Alkhateeb, Faisal, ed. Multi-Agent Systems - Modeling, Interactions, Simulations and Case Studies. InTech, 2011. http://dx.doi.org/10.5772/1936.

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19

Cognition and multi-agent interactions: From cognitive modeling to social simulation. New York: Cambridge University Press, 2005.

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20

Arjaliès, Diane-Laure, Philip Grant, Iain Hardie, Donald MacKenzie e Ekaterina Svetlova. Fund Managers and Their Investors. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198802945.003.0003.

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Chapter 3 examines the mechanisms through which clients impact fund managers’ practices and vice versa. The discussion encompasses fixed income investment as well as investment in shares. In both fixed income and shares, clients can include both institutional investors (such as pension funds) and retail investors (i.e. private individuals, though often guided by financial advisers). Their reasons for investment vary, leading to different time-horizons on their decisions, different ways of measuring performance, and different forms of interaction with the rest of the investment chain. They often rely on various types of advisers: investment consultants, independent financial advisers, and fund-rating companies. Variations of those kinds among the clients influence fund managers’ investment decisions, whether intentionally or not. Thus, the chapter suggests that the client–fund manager relationship is not a simple principal–agent problem, but a multi-faceted, contextually dependent, malleable matter.

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21

Smart, Paul R. Mandevillian Intelligence. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198801764.003.0013.

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Mandevillian intelligence is a specific form of collective intelligence in which individual cognitive shortcomings, limitations, and biases play a positive functional role in yielding various forms of collective cognitive success. When this idea is transposed to the epistemological domain, mandevillian intelligence emerges as the idea that individual forms of intellectual vice may, on occasion, support the epistemic performance of some form of multi-agent ensemble, such as a socio-epistemic system, a collective doxastic agent, or an epistemic group agent. As a specific form of collective intelligence, mandevillian intelligence is relevant to a number of debates in social epistemology, especially those that seek to understand how group (or collective) knowledge arises from the interactions between a collection of individual epistemic agents.

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22

Ehresmann, Andrée. Applications of Categories to Biology and Cognition. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198748991.003.0015.

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Mathematical models used in biology are generally adapted from physics and relate to specific local processes. Category theory helps developing global dynamic models account for the main specificities of living systems: (i) The system is evolutionary, with a tangled hierarchy of interacting components, which change over time. (ii) It develops a robust and flexible memory up to the emergence of components and processes of increasing complexity. (iii) It has a multi-agent, multi-temporality, self-organization. This chapter presents such a model, the Memory Evolutive Systems, which in particular characterizes the property at the root of emergence and flexibility. A main application is the model MENS for a neurocognitive system which proposes a physically based “theory of mind”, up to the emergence of higher cognitive processes such as consciousness, anticipation, and creativity.

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