Beer, R. D. (1995). A dynamical systems perspective on agent-environment interaction. Artificial Intelligence, 72(1-2):173--216. Home/Search Document Not in Database Summary Related Articles Check |
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The Importance of Viewing Cognition as the Result of.. - Nolfi, Baldassarre.. (2002) (Correct)
....its constituents parts, the body structure, and the environment. An important consequence of this view is that the agent and the environment constitutes a single system, i.e. the two aspects are so intimately connected that a description of each of them in isolation does not make much sense [1, 5]. By reviewing the results of a set of evolutionary experiments in which robots are free to develop their skills in close interaction with the environment [6] we will show that in many cases robots can solve complex problems in simple and effective ways by exploiting behaviors that emerge from ....
....scales, robots might need control systems able to work at different time rates. 2 Exploiting the interaction with the environment The behavior of embodied and situated organisms is an emergent result of the dynamical interaction between the nervous system, the body, and the external environment [5, 7]. This simple consideration has several important consequences that are far from being fully understood. One important aspect, for instance, is the fact that motor actions partially determine the sensory pattern that organisms receive from the environment. By coordinating sensory and motor ....
Beer R.D. (1995). A dynamical systems perspective on agent-environment interaction. Artificial Intelligence, 72:173-215.
Using Stigmergy to Make Emerging Collective Behaviors - D'Angelo, Pagello (2002) (Correct)
....The ability to trigger a group of robots to make emerging a specified collective behavior, such as a series of triangulations among soccer robots, is a current topic of research. To overcome a number of difficulties, it seems useful to refer to the studies of complex dynamical systems ( 12] [3]) where many robot related problems can be cast and interesting solutions can be obtained. No mathematical treatment ( 9] 11] 21] and [10] will be developed but a lot of qualitative methodologial schemas will be considered to control robot activity. Assuming each individual robot a ....
Randall D. Beer. A dynamical system perspective on agent-environment interaction. Artificial Intelligence, 72(1-2):173--215, 1995.
On Bots and Bacteria: Ontology Independent Embodiment - Quick, Dautenhahn.. (1999) (1 citation) (Correct)
....dynamics reflexively relate to the environments in which they are observed. Striking examples can be found in [1, 912 ] and particularly in Beer s work where particular emphasis is placed on the role of sensorimotor dynamics and structural coupling in autonomous behaviour in robotic systems [13, 14]. Sensorimotor coordination also features in research emphasising the evolution of neural control systems [15] Software Agents. Applying ideas about embodiment in the domain of software necessitates an ontological shift, simply because software is not based on the manipulation of physical ....
.... the body itself is seen as a dynamical system, body and mind can be grounded in a single phenomenon coupling between dynamical systems (cfi the Dynamical Hypothesis in Cognitive Science [21] 3 Embodiment as Structural Coupling between System and Environment Maturana and Varela s influential [13, 14] notion of structural coupling underlies the definition of embodiment presented here. Structural coupling is an ontologically nonspecific concept, arising as a consequence of mutual perturbation over time between structurally plastic systems. In their work, it is the basis of ontogenic variation ....
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Beer, R.D.: A dynamical systems perspective on agent-environment interaction. Art. Int. 72 (1995) 173-215
Synthesis of Autonomous Robots Through Evolution - Nolfi, Floreano (2002) (1 citation) (Correct)
....that this research area is making to the foundational debate in cognitive science. Exploiting the interaction with the environment The behavior of embodied and situated organisms is an emergent result of the dynamical interaction between the nervous system, the body, and the external environment [12, 13]. This simple consideration has several important consequences that are far from being fully understood. One important aspect, for instance, is the fact that motor actions partially determine the sensory pattern that organisms receive from the environment. By coordinating sensory and motor ....
....This can be explained by considering that, as we said above, behavior is the emergent result of the interactions between the individual and the environment. Given that in dynamical systems (for an introduction to dynamical systems and to dynamical approaches to the study of behavior see [13]) there is a complex and indirect relation between the rules that determine the interactions and the emergent result of those interactions, it is very difficult to identify how the interactions between the organism and the external environments contribute to the resulting behavior. As a ....
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Beer, R. (1995) A dynamical systems perspective on agent-environment interaction Artificial Intelligence 72, 173-215
Evolving Robots Able To Integrate Sensory-Motor Information.. - Nolfi, Marocco (2001) (Correct)
.... sensory motor information over time play a limited role) In this paper we will discuss in which conditions we can expect the emergence of systems able to integrate sensory motor information over time and later use this information to modulate their behavior accordingly (for a similar view see [Beer, 1995, 1996] In doing so we will discuss the problems that these systems should be able to solve and the processes that might lead to a transition from simple agents that only rely on sensory information or on their internal dynamic to system that are also able to integrate information over time. In ....
....whether internal states are internal representation. This can be explained by considering that: a) There is no clear way to identify whether an internal state has a representational status or whether it simply display an accidental correlation with some feature of the external environment (Beer, 1995; Clark 1997) 3 (b) Natural organisms and artificial organisms that are left free to self organize tend to have internal states that are local and action oriented rather than objective and action independent. Identifying what is the relation between such states and the external environment is ....
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Beer R.D. (1995). A dynamical systems perspective on agent-environment interaction. Artificial Intelligence, 72:173-215.
Maximizing Reward in a Non-Stationary Mobile Robot Environment - Goldberg, Mataric (2002) (2 citations) (Correct)
....decision process for modeling industrial manufacturing. The goal is to optimize production using reinforcement learning. Other work has also used such hybrid SMP MDP models, or semi Markov decision processes (Sutton et al. 1998; Wang and Mahadevan, 1999) as well as dynamical systems approaches (Beer, 1993; Smithers, 1995) to model the interaction between an agent (robot) and its environment. The basic structure of augmented Markov models is very similar to that of hidden Markov models (HMMs) Rabiner, 1989) The di erence is that in a AMM, there is only one observation symbol per state, as ....
Beer, R. D.: 1993, `A Dynamical Systems Perspective on Agent-Environment Interaction'. Articial Intelligence 72, 173-215.
A Connectionist Model Which Unifies the Behavioral and the.. - Sugita, TANI (2000) (Correct)
....a ord to utilize the internal modeling. On the other hand, some studies of the cognitive robotics, conducted by Tani and the others[17, 18] gives an instance of the latter strategy, in which the role of dynamical systems in cognition are emphasized with following the ideas of Pollack[13] and Beer[1]. These researches showed that the internal model of the environment could be represented as embedded in the attractor of the internal neural dynamics. Concretely, time series of the robot sensory motor information, that is generated through robot s interaction with the environment, is learned as ....
R.D. Beer. A dynamical systems perspective on agent-environment interaction. Articial Intelligence, 72(1):173-215, 1995.
Dynamic Approaches to Cognition - Van Gelder (Correct)
....ground between pure GOFAI and an equally extreme dynamicism (van Gelder forthcoming) How does the dynamical approach relate to connectionism In a word, they overlap. Connectionist networks are generally dynamical systems, and much of the best dynamical research is connectionist in form (e.g. Beer 1995). However, the way many connectionists structure and interpret their systems is dominated by broadly computational preconceptions (e.g. Rosenberg and Sejnowski 1987) Conversely, many dynamical models of cognition are not connectionist networks. Connectionism is best seen as straddling a more ....
Beer, R. D. (1995). A dynamical systems perspective on agentenvironment interaction. Artificial Intelligence 72: 173--215.
Autopoiesis and Cognition in the - Game Of Life Self-citation (Beer) (Correct)
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Beer, R. D. (1995). A dynamical systems perspective on agent-environment interaction. Artificial Intelligence, 72, 173--215.
The Dynamics of Active Categorical Perception in an Evolved Model.. - Beer (2003) (5 citations) Self-citation (Beer) (Correct)
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Beer, R. D. (1995a). A dynamical systems perspective on agent--environment interaction. Artificial Intelligence, 72, 173--215.
Beyond Control: The Dynamics of Brain-Body-Environment Interaction .. - Beer (2006) Self-citation (Beer) (Correct)
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Beer, R.D. (1995a). A dynamical systems perspective on agent-environment interaction. Artificial Intelligence 72:173-215.
Evolving Dynamical Systems: Nearly Neutral Regions in.. - Izquierdo-Torres (2003) Self-citation (Beer) (Correct)
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Beer, R. D. (1995a). A dynamical system perspective on agent-environment interaction. Artificial Intelligence, 72, 173--215.
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Beer, R. D. (1995). A dynamical systems perspective on agent-environment interaction. Artificial Intelligence, 72(1-2):173--216.
Pro-active agents - With Recurrent Neural (Correct)
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R.D. Beer. A dynamical systems perspective on agent-environment interaction. Artificial Intelligence 72, pages 173--215, 1995.
Towards Pro-active Embodied Agents: - On The Importance (Correct)
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R.D. Beer. A dynamical systems perspective on agent-environment interaction. Artificial Intelligence 72, pages 173--215, 1995.
Journal of Computational Neuroscience 7, 119--147 (1999) c - Evolution And Analysis (Correct)
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Beer RD (1995) A dynamical systems perspective on agentenvironment interaction. Artificial Intell. 72:173--215.
Synamic Approaches to Cognitive Science - Beer (Correct)
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Beer, R.D. (1995) A dynamical systems perspective on agent--environment interaction. Artif. Intell. 72, 173--215
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R.D. Beer. (1995) A dynamical system perspective on agent-environment interaction. In Artificial Intelligence 72. 173-215.
Collective Intelligence in Multi-Agent Robotics: Stigmergy.. - Izquierdo-Torres (2004) (Correct)
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RD Beer. A dynamical systems perspective on agent-environment interaction. Artificial Intelligence 72:173-215, 1995. 9
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Beer, R.D. 1995b. A dynamical system perspective on agentenvironment interaction. In Artificial Intelligence 72. 173215.
Continuous-Spaced Action Selection for Single- and.. - Low, Leow, Ang, Jr. (2004) (Correct)
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R. D. Beer. A dynamical systems perspective on agentenvironment interaction. Artificial Intelligence, 72(12) :173--215, 1995.
Embodied Artificial Intelligence - Chrisley (2003) (2 citations) (Correct)
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R. Beer, A dynamical systems perspective on agent-environment interaction, in: R.L. Chrisley (Ed.), Artificial Intelligence: Critical Concepts, Vol. III, Routledge, London, 2000.
Creatures: Entertainment Software Agents with Artificial Life - Grand, Cliff (1997) (26 citations) (Correct)
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R. D. Beer, "A Dynamical Systems Perspective on agent-environment interaction ", Artificial Intelligence 72:173--215.
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Randall D. Beer (1995) A dynamical systems perspective on agent-environment interaction. Artificial Intelligence 72(1/2), pp.173-216.
Mobile Robot Group Coordination Using a Model of Interaction .. - Goldberg, Mataric (Correct)
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R. D. Beer, "A dynamical systems perspective on agent-environment interaction," Artificial Intelligence 72, pp. 173--215, 1993.
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