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Letteratura scientifica selezionata sul tema "Artificial adaptive agents"

Autore: Grafiati

Pubblicato: 4 giugno 2021

Ultima modifica: 12 febbraio 2022

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Articoli di riviste sul tema "Artificial adaptive agents"

Andreoni, James, e John H. Miller. "Auctions with Artificial Adaptive Agents". Games and Economic Behavior 10, n. 1 (luglio 1995): 39–64. http://dx.doi.org/10.1006/game.1995.1024.

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Maes, Pattie. "Modeling Adaptive Autonomous Agents". Artificial Life 1, n. 1_2 (ottobre 1993): 135–62. http://dx.doi.org/10.1162/artl.1993.1.1_2.135.

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One category of research in Artificial Life is concerned with modeling and building so-called adaptive autonomous agents, which are systems that inhabit a dynamic, unpredictable environment in which they try to satisfy a set of time-dependent goals or motivations. Agents are said to be adaptive if they improve their competence at dealing with these goals based on experience. Autonomous agents constitute a new approach to the study of Artificial Intelligence (AI), which is highly inspired by biology, in particular ethology, the study of animal behavior. Research in autonomous agents has brought about a new wave of excitement into the field of AI. This paper reflects on the state of the art of this new approach. It attempts to extract its main ideas, evaluates what contributions have been made so far, and identifies its current limitations and open problems.

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Wan, Hakman A., e Andrew Hunter. "On Artificial Adaptive Agents Models of Stock Markets". SIMULATION 68, n. 5 (maggio 1997): 279–89. http://dx.doi.org/10.1177/003754979706800503.

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Steels, Luc. "The Artificial Life Roots of Artificial Intelligence". Artificial Life 1, n. 1_2 (ottobre 1993): 75–110. http://dx.doi.org/10.1162/artl.1993.1.1_2.75.

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Behavior-oriented Artificial Intelligence (AI) is a scientific discipline that studies how behavior of agents emerges and becomes intelligent and adaptive. Success of the field is defined in terms of success in building physical agents that are capable of maximizing their own self-preservation in interaction with a dynamically changing environment. The paper addresses this Artificial Life route toward AI and reviews some of the results obtained so far.

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MERTOGUNO, J. SUKARNO. "DISTRIBUTED KNOWLEDGE-BASE: ADAPTIVE MULTI-AGENTS APPROACH". International Journal on Artificial Intelligence Tools 07, n. 01 (marzo 1998): 59–70. http://dx.doi.org/10.1142/s0218213098000056.

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This paper describes an approach of using Multi-agents theory to construct an adaptive knowledge base system. To represent the knowledge, frame base (graph) knowledge representation has been chosen. The driving force of this study is the intention of having a distributed (possibly across the net) adaptive knowledge base. The challenge of developing an adaptive knowledge base lays on how to evolve the knowledge (adaptability) and how to control the evolution (maintain the quality of the knowledge). In this paper the manifestation of both of the issues above on our model will be addressed.

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Ziemke, Tom. "Adaptive Behavior in Autonomous Agents". Presence: Teleoperators and Virtual Environments 7, n. 6 (dicembre 1998): 564–87. http://dx.doi.org/10.1162/105474698565947.

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This paper provides an overview of the bottom-up approach to artificial intelligence (AI), commonly referred to as behavior-oriented AI. The behavior-oriented approach, with its focus on the interaction between autonomous agents and their environments, is introduced by contrasting it with the traditional approach of knowledge-based AI. Different notions of autonomy are discussed, and key problems of generating adaptive and complex behavior are identified. A number of techniques for the generation of behavior are introduced and evaluated regarding their potential for realizing different aspects of autonomy as well as adaptivity and complexity of behavior. It is concluded that, in order to realize truly autonomous and intelligent agents, the behavior-oriented approach will have to focus even more on lifelike qualities in both agents and environments.

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Karpov, Valery. "On moral aspects of adaptive behavior of artificial agents". Artificial societies 16, n. 2 (2021): 0. http://dx.doi.org/10.18254/s207751800014740-3.

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This article describes a model of a social agent, whose behavior can be stated in terms of basic moral mechanisms and norms. Morality is considered here as a flexible adaptive mechanism that allows agents to vary behavior depending on the environment conditions. The control system of the social agent is based on the "emotion-requirement" architecture. Together with the mechanisms of imitative behavior and the identification of other observable agents with the subjective "Me" concept, this architecture allows to interpret the agent's behavior in terms of empathy, sympathy, and friend-foe relationships. Experiments with this model are described, the main variable parameter of which was the tendency to sympathy. The objective of the experiments was to determine the dependence of the group "well-being" indicators on their altruism. The results obtained are quite consistent with the well-known sociological conclusions, which made it possible to say that the proposed behavioral models and architecture of agents are adequate to intuitive ideas about the role and essence of morality. Thus, the possibility of transition in this area from abstract humanitarian reasoning to constructive schemes and models of adaptive behavior of artificial agents was demonstrated.

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Pereira, Ademir Rodrigues, e Liu Hsu. "Adaptive Formation Control using Artificial Potentials for Euler-Lagrange Agents". IFAC Proceedings Volumes 41, n. 2 (2008): 10788–93. http://dx.doi.org/10.3182/20080706-5-kr-1001.01829.

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Bullard, James, e John Duffy. "A model of learning and emulation with artificial adaptive agents". Journal of Economic Dynamics and Control 22, n. 2 (febbraio 1998): 179–207. http://dx.doi.org/10.1016/s0165-1889(97)00072-9.

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Vrancx, Peter, Enda Howley e Matt Knudson. "Preface to the special issue: adaptive learning agents". Knowledge Engineering Review 31, n. 1 (gennaio 2016): 1–2. http://dx.doi.org/10.1017/s0269888915000144.

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Tesi sul tema "Artificial adaptive agents"

Neves, Pedro. "An Implementation Framework for Emotion Based Adaptive Agents". Master's thesis, Department of Informatics, University of Lisbon, 2008. http://hdl.handle.net/10451/14023.

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The work presented in this document is part of the project AutoFocus: Adaptive Self-Improving Multi-Agent Systems' that is being developed at the research unit LabMAg, which objective is the implementation of multi-agent systems based on autonomous entities capable of self-optimized and adaptive behaviors. The notion of autonomic computation, like other notions that also imply pro-active computation, is based on autonomous entities that actively work to achieve their objectives and have the ability to dynamically adjust to changes in their environment, constrained by time and resource limits. In the approach used by the AutoFocus project, that adaptation to change and the regulation of the agent's capabilities, result from the combination of cognitive aspects with emotional based aspects. The agent model defined and used by the AutoFocus project is the Agent Flow Model. The task that corresponded to the work presented in this document was to develop a platform for the Agent Flow Model. It was intended, with this platform, to provide a tool that enables the rapid deployment and monitoring of agents based on this model. The developed work consisted in the analysis and design, oriented to objects, implementation and testing of components of this platform

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Rylatt, R. Mark. "Investigations into controllers for adaptive autonomous agents based on artificial neural networks". Thesis, De Montfort University, 2001. http://hdl.handle.net/2086/10734.

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This thesis reports the development and study of novel architectures for the simulation of adaptive behaviour based on artificial neural networks. There are two distinct levels of enquiry. At the primary level, the initial aim was to design and implement a unified architecture integrating sensorimotor learning and overall control. This was intended to overcome shortcomings of typical behaviour-based approaches in reactive control settings. It was achieved in two stages. Initially, feedforward neural networks were used at the sensorimotor level of a modular architecture and overall control was provided by an algorithm. The algorithm was then replaced by a recurrent neural network. For training, a form of reinforcement learning was used. This posed an intriguing composite of the well-known action selection and credit assignment problems. The solution was demonstrated in two sets of simulation studies involving variants of each architecture. These studies also showed: firstly that the expected advantages over the standard behaviour-based approach were realised, and secondly that the new integrated architecture preserved these advantages, with the added value of a unified control approach. The secondary level of enquiry addressed the more foundational question of whether the choice of processing mechanism is critical if the simulation of adaptive behaviour is to progress much beyond the reactive stage in more than a trivial sense. It proceeded by way of a critique of the standard behaviourbased approach to make a positive assessment of the potential for recurrent neural networks to fill such a role. The findings were used to inform further investigations at the primary level of enquiry. These were based on a framework for the simulation of delayed response learning using supervised learning techniques. A further new architecture, based on a second-order recurrent neural network, was designed for this set of studies. It was then compared with existing architectures. Some interesting results are presented to indicate the appropriateness of the design and the potential of the approach, though limitations in the long run are not discounted.

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Bagnall, A. J. "Modelling the UK market in electricity generation with autonomous adaptive agents". Thesis, University of East Anglia, 2000. https://ueaeprints.uea.ac.uk/21583/.

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The modern trend in electricity industries around the world is towards privatisation. Increased competition, it is argued, will ultimately benefit the consumer. However, the particular nature of electricity generation and supply means strong regulation of a privatised market will always be necessary. In establishing a privatised industry, decisions need to be made about the mechanisms governing the requirements to meet demand, to maintain the viability of the network and to ensure generators are paid correctly for power generated. Unfortunately, it is unclear what processes to use to achieve these goals while still delivering some benefit to the consumer in the form of reduced electricity costs. This research, sponsored by the National Grid Company, examines whether the application of new ideas in artificial intelligence could offer the potential for gaining insights into the affects of certain market mechanisms on the competitors in the market. Our approach to gaining greater understanding into how the market operates is to adopt an evolutionary economics perspective. We have constructed autonomous adaptive agents to represent the generating companies in a simplified model of the UK market in electricity generation. The main body of the thesis contains a description of the process of developing the model and the agent architecture. Once we were satisfied that the model incorporated some key features of the real world market and that the agents, based on learning classifier systems, were able to perform well in simpler environments, we examined how multiple adaptive agents learn to interact in the simplified model. We conclude that the agents are able to learn how to behave in ways analogous to the observed behaviour of real world generating companies. We then illustrate the potential for this type of economic model by examining how alterations to market structure affect agent behaviour, and investigate to what extent the agents are able to learn how to cooperate for mutual long term benefit.

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Fent, Thomas. "Adaptive agents in the House of Quality". SFB Adaptive Information Systems and Modelling in Economics and Management Science, WU Vienna University of Economics and Business, 1999. http://epub.wu.ac.at/1556/1/document.pdf.

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Managing the information flow within a big organization is a challenging task. Moreover, in a distributed decision-making process conflicting objectives occur. In this paper, artificial adaptive agents are used to analyze this problem. The decision makers are implemented as Classifier Systems, and their learning process is simulated by Genetic Algorithms. To validate the outcomes we compared the results with the optimal solutions obtained by full enumeration. It turned out that the genetic algorithm indeed was able to generate useful rules that describe how the decision makers involved in new product development should react to the requests they are required to fulfill. (author's abstract)
Series: Working Papers SFB "Adaptive Information Systems and Modelling in Economics and Management Science"

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Fountoukidis, Dimitrios P. "Adaptive management of emerging battlefield network". Thesis, Monterey, Calif. : Springfield, Va. : Naval Postgraduate School ; Available from National Technical Information Service, 2004. http://library.nps.navy.mil/uhtbin/hyperion/04Mar%5FFountoukidis.pdf.

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Thesis (M.S. in Information Technology Management and M.S. in Modeling Virtual Environment and Simulation)--Naval Postgraduate School, March 2004.
Thesis advisor(s): Alex Bordetsky, John Hiles. Includes bibliographical references. Also available online.

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Mazzapioda, Mariagiovanna. "On the evolutionary co-adaptation of morphology and distributed neural controllers in adaptive agents". Thesis, University of Plymouth, 2012. http://hdl.handle.net/10026.1/1011.

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The attempt to evolve complete embodied and situated artificial creatures in which both morphological and control characteristics are adapted during the evolutionary process has been and still represents a long term goal key for the artificial life and the evolutionary robotics community. Loosely inspired by ancient biological organisms which are not provided with a central nervous system and by simple organisms such as stick insects, this thesis proposes a new genotype encoding which allows development and evolution of mor- phology and neural controller in artificial agents provided with a distributed neural network. In order to understand if this kind of network is appropriate for the evolution of non trivial behaviours in artificial agents, two experiments (description and results will be shown in chapter 3) in which evolution was applied only to the controller’s parameters were performed. The results obtained in the ﬁrst experiment demonstrated how distributed neural networks can achieve a good level of organization by synchronizing the output of oscillatory elements exploiting acceleration/deceleration mechanisms based on local interactions. In the second experiment few variants on the topology of neural architecture were introduced. Results showed how this new control system was able to coordinate the legs of a simulated hexapod robot on two diﬀerent gaits on the basis of the external circumstances. After this preliminary and successful investigation, a new genotype encoding able to develop and evolve artificial agents with no ﬁxed morphology and with a distributed neural controller was proposed. A second set of experiments was thus performed and the results obtained conﬁrmed both the eﬀectiveness of genotype encoding and the ability of distributed neural network to perform the given task. The results have also shown the strength of genotype both in generating a wide range of diﬀerent morphological structures and in favouring a direct co-adaptation between neural controller and morphology during the evolutionary process. Furthermore the simplicity of the proposed model has showed the eﬀective role of speciﬁc elements in evolutionary experiments. In particular it has demonstrated the importance of the environment and its complexity in evolving non-trivial behaviours and also how adding an independent component to the ﬁtness function could help the evolutionary process exploring a larger space solutions avoiding a premature convergence towards suboptimal solutions.

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Kochenderfer, Mykel J. "Adaptive modelling and planning for learning intelligent behaviour". Thesis, University of Edinburgh, 2006. http://hdl.handle.net/1842/1408.

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An intelligent agent must be capable of using its past experience to develop an understanding of how its actions affect the world in which it is situated. Given some objective, the agent must be able to effectively use its understanding of the world to produce a plan that is robust to the uncertainty present in the world. This thesis presents a novel computational framework called the Adaptive Modelling and Planning System (AMPS) that aims to meet these requirements for intelligence. The challenge of the agent is to use its experience in the world to generate a model. In problems with large state and action spaces, the agent can generalise from limited experience by grouping together similar states and actions, effectively partitioning the state and action spaces into finite sets of regions. This process is called abstraction. Several different abstraction approaches have been proposed in the literature, but the existing algorithms have many limitations. They generally only increase resolution, require a large amount of data before changing the abstraction, do not generalise over actions, and are computationally expensive. AMPS aims to solve these problems using a new kind of approach. AMPS splits and merges existing regions in its abstraction according to a set of heuristics. The system introduces splits using a mechanism related to supervised learning and is defined in a general way, allowing AMPS to leverage a wide variety of representations. The system merges existing regions when an analysis of the current plan indicates that doing so could be useful. Because several different regions may require revision at any given time, AMPS prioritises revision to best utilise whatever computational resources are available. Changes in the abstraction lead to changes in the model, requiring changes to the plan. AMPS prioritises the planning process, and when the agent has time, it replans in high-priority regions. This thesis demonstrates the flexibility and strength of this approach in learning intelligent behaviour from limited experience.

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Jones, Joshua K. "Empirically-based self-diagnosis and repair of domain knowledge". Diss., Georgia Institute of Technology, 2009. http://hdl.handle.net/1853/33931.

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In this work, I view incremental experiential learning in intelligent software agents as progressive agent self-adaptation. When an agent produces an incorrect behavior, then it may reflect on, and thus diagnose and repair, the reasoning and knowledge that produced the incorrect behavior. In particular, I focus on the self-diagnosis and self-repair of an agent's domain knowledge. The implementation of systems with the capability to self-diagnose and self-repair involves building both reasoning processes capable of such learning and knowledge representations capable of supporting those reasoning processes. The core issue my dissertation addresses is: what kind of metaknowledge (knowledge about knowledge) may enable the agent to diagnose faults in its domain knowledge? In providing a solution to this issue, the central contribution of this research is a theory of the kind of metaknowledge that enables a system to reason about and adapt its conceptual knowledge. For this purpose, I propose a representation that explicitly encodes metaknowledge in the form of procedures called Empirical Verification Procedures (EVPs). In the proposed knowledge representation, an EVP is associated with each concept within the agent's domain knowledge. Each EVP explicitly semantically grounds the associated concept in the agent's perception, and can thus be used as a test to determine the validity of knowledge of that concept during diagnosis. I present the formal and empirical evaluation of a system, Augur, that makes use of EVP metaknowledge to adapt its own domain knowledge in the context of a particular subclass of classification problem that I call compositional classification, in which the overall classification task can be broken into a hierarchically organized set of subtasks. I hypothesize that EVP metaknowledge will enable a system to automatically adapt its knowledge in two ways: first, by adjusting the ways that inputs are categorized by a concept, in accordance with semantics fixed by an associated EVP; and second, by adjusting the semantics of concepts themselves when they fail to contribute appropriately to system goals. The latter adaptation is realized by altering the EVP associated with the concept in question. I further hypothesize that the semantic grounding of domain concepts in perception through the use of EVPs will increase the generalization power of a learner that operates over those concepts, and thus make learning more efficient. Beyond the support of these hypotheses, I also present results pertinent to the understanding of learning in compositional classification settings using structured knowledge representations.

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Schäfer, Boris. "Detached tool use in evolutionary robotics : Evolving tool use skills". Thesis, University of Skövde, School of Humanities and Informatics, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-12.

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This master thesis investigates the principal capability of artificial evolution to produce tool use behavior in adaptive agents, excluding the application of life-time learning or adaptation mechanisms. Tool use is one aspect of complex behavior that is expected from autonomous agents acting in real-world environments. In order to achieve tool use behavior an agent needs to identify environmental objects as potential tools before it can use the tools in a problem-solving task. Up to now research in robotics has focused on life-time learning mechanisms in order to achieve this. However, these techniques impose great demands on resources, e.g. in terms of memory or computational power. All of them have shown limited results with respect to a general adaptivity. One might argue that even nature does not present any kind of omni-adaptive agent. While humans seem to be a good example of natural agents that master an impressive variety of life conditions and environments (at least from a human perspective, other examples are spectacular survivability observations of octopuses, scorpions or various viruses) even the most advanced engineering approaches can hardly compete with the simplest life-forms in terms of adaptation. This thesis tries to contribute to engineering approaches by promoting the application of artificial evolution as a complementing element with the presentation of successful pioneering experiments. The results of these experiments show that artificial evolution is indeed capable to render tool use behavior at different levels of complexity and shows that the application of artificial evolution might be a good complement to life-time approaches in order to create agents that are able to implicitly extract concepts and display tool use behavior. The author believes that off-loading at least parts of the concept retrieval process to artificial evolution will reduce resource efforts at life-time when creating autonomous agents with complex behavior such as tool use. This might be a first step towards the vision of a higher level of autonomy and adaptability. Moreover, it shows the demand for an experimental verification of commonly accepted limits between qualities of learned and evolved tool use capabilities.

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Angus, Simon Douglas Economics Australian School of Business UNSW. "Economic networks: communication, cooperation & complexity". Awarded by:University of New South Wales. Economics, 2007. http://handle.unsw.edu.au/1959.4/27005.

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This thesis is concerned with the analysis of economic network formation. There are three novel sections to this thesis (Chapters 5, 6 and 8). In the first, the non-cooperative communication network formation model of Bala and Goyal (2000) (BG) is re-assessed under conditions of no inertia. It is found that the Strict Nash circle (or wheel) structure is still the equilibrium outcome for n = 3 under no inertia. However, a counter-example for n = 4 shows that with no inertia infinite cycles are possible, and hence the system does not converge. In fact, cycles are found to quickly dominate outcomes for n > 4 and further numerical simulations of conditions approximating no inertia (probability of updating > 0.8 to 1) indicate that cycles account for a dramatic slowing of convergence times. These results, together with the experimental evidence of Falk and Kosfeld (2003) (FK) motivate the second contribution of this thesis. A novel artificial agent model is constructed that allows for a vast strategy space (including the Best Response) and permits agents to learn from each other as was indicated by the FK results. After calibration, this model replicates many of the FK experimental results and finds that an externality exploiting ratio of benefits and costs (rather than the difference) combined with a simple altruism score is a good proxy for the human objective function. Furthermore, the inequity aversion results of FK are found to arise as an emergent property of the system. The third novel section of this thesis turns to the nature of network formation in a trust-based context. A modified Iterated Prisoners' Dilemma (IPD) model is developed which enables agents to play an additional and costly network forming action. Initially, canonical analytical results are obtained despite this modification under uniform (non-local) interactions. However, as agent network decisions are 'turned on' persistent cooperation is observed. Furthermore, in contrast to the vast majority of non-local, or static network models in the literature, it is found that a-periodic, complex dynamics result for the system in the long-run. Subsequent analysis of this regime indicates that the network dynamics have fingerprints of self-organized criticality (SOC). Whilst evidence for SOC is found in many physical systems, such dynamics have been seldom, if ever, reported in the strategic interaction literature.

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Libri sul tema "Artificial adaptive agents"

Karl, Tuyls, ALAMAS 2006 (2006 : Brussels, Belgium) e ALAMAS 2007 (2007 : Maastricht, Netherlands), a cura di. Adaptive agents and multi-agent systems III: Adaptation and multi-agent learning ; 5th, 6th, and 7th European Symposium, ALAMAS 2005-2007 on Adaptive and Learning Agents and Multi-Agent Systems : revised selected papers. Berlin: Springer, 2008.

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Rylatt, R. Mark. Investigations into controllers for adaptive autonomous agents based on artificial neural networks. Leicester: De Montfort University, 2001.

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author, Shi Dianxi, a cura di. Ruan jian shi ying xing ji shu: Cong ge ti shi ying dao qun ti shi ying. Beijing: Ke xue chu ban she, 2013.

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Sugumaran, Vijayan. Intelligent, adaptive and reasoning technologies: New developments and applications. Hershey, PA: Information Science Reference, 2011.

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Matthew, Knudson, Grześ Marek e SpringerLink (Online service), a cura di. Adaptive and Learning Agents: International Workshop, ALA 2011, Held at AAMAS 2011, Taipei, Taiwan, May 2, 2011, Revised Selected Papers. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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Taylor, Matthew E. Adaptive and Learning Agents: Second Workshop, ALA 2009, Held as Part of the AAMAS 2009 Conference in Budapest, Hungary, May 12, 2009. Revised Selected Papers. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010.

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Mamei, Marco. Field-based coordination for pervasive multiagent systems. Berlin: Springer, 2005.

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Adaptive bidding in single sided auctions under uncertainty: An agent-based approach in market engineering. Basel: Birkhauser Verlag AG, 2007.

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International, Joint Conference on Artificial Intelligence (14th 1995 Montréal Québec). Adaption and learning in multi-agent systems: IJCAI '95 workshop, Montréal, Canada, August 21, 1995, proceedings. Berlin: Springer, 1996.

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A, Vouros George, a cura di. Organized adaption in multi-agent systems: First international workshop, OAMAS 2008, Estoril, Portugal, May 13, 2008 : revised and invited papers. Berlin: Springer-Verlag, 2009.

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Capitoli di libri sul tema "Artificial adaptive agents"

Bosse, Tibor, Charlotte Gerritsen, Jeroen de Man e Suzanne Tolmeijer. "Adaptive Training for Aggression de-Escalation". In Artificial Life and Intelligent Agents, 80–93. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-18084-7_7.

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Ishida, Takashi, Hiroshi Yokoi e Yukinori Kakazu. "Emergence of Adaptive Society with Competitive Selfish Agents". In Advances in Artificial Life, 555–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 1999. http://dx.doi.org/10.1007/3-540-48304-7_74.

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Boulehouache, Soufiane, Ramdane Maamri e Zaidi Sahnoun. "Toward Component-Based Self-Adaptive Multi-Strategic Pedagogical Agents". In Artificial Intelligence XXXIV, 169–74. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71078-5_16.

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Red’ko, Vladimir G., Oleg P. Mosalov e Danil V. Prokhorov. "Investigation of Evolving Populations of Adaptive Agents". In Artificial Neural Networks: Biological Inspirations – ICANN 2005, 337–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11550822_53.

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Lopes Cardoso, Henrique, e Eugénio Oliveira. "Using and Evaluating Adaptive Agents for Electronic Commerce Negotiation". In Advances in Artificial Intelligence, 96–105. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/3-540-44399-1_11.

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Damoulas, Theodoros, Ignasi Cos-Aguilera, Gillian M. Hayes e Tim Taylor. "Valency for Adaptive Homeostatic Agents: Relating Evolution and Learning". In Advances in Artificial Life, 936–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11553090_94.

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Ganon, Zohar, Alon Keinan e Eytan Ruppin. "Evolutionary Network Minimization: Adaptive Implicit Pruning of Successful Agents". In Advances in Artificial Life, 319–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-39432-7_34.

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Legéndi, Richárd, László Gulyás, Rajmund Bocsi e Tamás Máhr. "Modeling Autonomous Adaptive Agents with Functional Language for Simulations". In Progress in Artificial Intelligence, 449–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04686-5_37.

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Pacheco, Jorge M., Tom Lenaerts e Francisco C. Santos. "Evolution of Cooperation in a Population of Selfish Adaptive Agents". In Advances in Artificial Life, 535–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-74913-4_54.

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Vilà, Xavier. "Adaptive Artificial Agents Play a Finitely Repeated Discrete Principal-Agent Game". In Lecture Notes in Economics and Mathematical Systems, 437–56. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-662-03366-1_35.

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Atti di convegni sul tema "Artificial adaptive agents"

"Adaptive Agents for Cyber-Physical Systems". In International Conference on Agents and Artificial Intelligence. SciTePress - Science and and Technology Publications, 2013. http://dx.doi.org/10.5220/0004244402570262.

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Frasheri, Mirgita, Baran Cürüklü e Mikael Ekstroem. "Towards Collaborative Adaptive Autonomous Agents". In 9th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2017. http://dx.doi.org/10.5220/0006195500780087.

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Ranjbar Sahraei, Bijan, Alireza Nemati, Mehdi Farshchi e Ali Meghdari. "Adaptive Fuzzy Sliding Mode Control Approach for Swarm Formation Control of Multi-Agent Systems". In ASME 2010 10th Biennial Conference on Engineering Systems Design and Analysis. ASMEDC, 2010. http://dx.doi.org/10.1115/esda2010-25371.

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Abstract (sommario):

In this paper, an adaptive control scheme for multi-agent formation control is proposed. This control method is based on artificial potential functions integrated with adaptive fuzzy sliding mode control technique. We consider fully actuated mobile agents with completely unknown dynamics. An adaptive fuzzy logic system is used to approximate the unknown system dynamics. Sliding Mode Control (SMC) theory is used to force agents’ motion to obey the dynamics defined by the simple inter-agent artificial potential functions. Stability proof is given using Lyapunov functions, which shows the robustness of controller with respect to disturbances and system uncertainties. Simulation results are demonstrated for a multi-agent formation problem, illustrating the effectiveness of the proposed method. Experimental results are included to verify the applicability of the scheme for a test-bed of six real mobile robots.

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"Self-adaptive Topology Neural Network for Online Incremental Learning". In International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and and Technology Publications, 2014. http://dx.doi.org/10.5220/0004811500940101.

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"A Multi-demand Adaptive Bargaining based on Fuzzy Logic". In International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and and Technology Publications, 2014. http://dx.doi.org/10.5220/0004907005770585.

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"Agent-based Simulations of Patterns for Self-adaptive Systems". In International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and and Technology Publications, 2014. http://dx.doi.org/10.5220/0004925001900200.

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Benomrane, Souad, Zied Sellami, Mounir Ben Ayed e Adel M. Alimi. "An Adaptive Multi-Agent System for Ontology Co-evolution". In International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and and Technology Publications, 2015. http://dx.doi.org/10.5220/0005257602160221.

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"Self-adaptive Aided Decision-making - Application to Maritime Surveillance". In International Conference on Agents and Artificial Intelligence. SciTePress - Science and and Technology Publications, 2013. http://dx.doi.org/10.5220/0004260404190422.

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Noblega, Ashey, Aline Paes e Esteban Clua. "Towards Adaptive Deep Reinforcement Game Balancing". In 11th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2019. http://dx.doi.org/10.5220/0007395406930700.

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Conn, Marvin, e Darsana Josyula. "Adaptive Classifiers: Applied to Radio Waveforms". In 12th International Conference on Agents and Artificial Intelligence. SCITEPRESS - Science and Technology Publications, 2020. http://dx.doi.org/10.5220/0009180609870994.

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Rapporti di organizzazioni sul tema "Artificial adaptive agents"

Bullard, James, e John Duffy. A Model of Learning and Emulation with Artificial Adaptive Agents. Federal Reserve Bank of St. Louis, 1994. http://dx.doi.org/10.20955/wp.1994.014.

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