http://popups.lib.uliege.be/1373-5411/index.php?id=1442 Index terms fr 0 http://popups.lib.uliege.be/1373-5411/index.php?id=1646 The architecture, Complexity and Organized Behaviour Within Environmental Bounds (COBWEB), was developed to support an anticipatory approach to adaptation. COBWEB consists of a large number of autonomous agents, each a genetic algorithm, using different strategies to adapt to changing resource availability. Anticipatory genetic algorithms, that are Turing complete, as well as mutation are used to allow the agents to respond to a changing environment. The simulation has four attractors, which exhibit sensitivity to initial conditions, and the spatial patterns of the agents exhibit wide variation as well as local structure, which might indicate adapive or anticipatory behaviour Mon, 15 Jul 2024 16:15:28 +0200 Tue, 08 Oct 2024 14:35:57 +0200 http://popups.lib.uliege.be/1373-5411/index.php?id=1646 http://popups.lib.uliege.be/1373-5411/index.php?id=3926 Organizational systems are one of the more remarkable classes of weak anticipative systems in which decision making is the main force for its functioning and development. Simulation-based decision support is a holistic methodology for decision assessment in organizations. System dynamics is a proper methodology for modeling and testing the dynamic hypotheses of organizational systems. The role of subjects in model development and its validation from cybernetic, psychological and cognitive perspectives are discussed in this paper. Group participation in model building and validation is suggested in order to prevent the manipulation of dominant subjects and/or implicit dictators during modeling. This paper concludes with some useful examples of systems simulation in solving real problems. Tue, 01 Oct 2024 15:27:54 +0200 Tue, 01 Oct 2024 15:28:13 +0200 http://popups.lib.uliege.be/1373-5411/index.php?id=3926 http://popups.lib.uliege.be/1373-5411/index.php?id=2839 The paper presents a model of heuristic negotiation between self-interested agents which allows the use of arguments, negotiation over multiple issues of the negotiation object, single and multi-party negotiation, and learning of the agent's negotiation primitives. The model uses negotiation objects and negotiation frames to separate the object of negotiation from the negotiation process. In order to negotiate strategically, the agents use a reinforcement learning algorithm applied on a specific state space representation of the negotiation process. Tue, 03 Sep 2024 15:20:24 +0200 Tue, 03 Sep 2024 15:21:51 +0200 http://popups.lib.uliege.be/1373-5411/index.php?id=2839 http://popups.lib.uliege.be/1373-5411/index.php?id=2622 The ability to anticipate future states is a key adaptive property of living systems (Glenberg, 1997). Robert Rosen (1985) suggested that an anticipatory system is characterized by finality, and "is a system containing a predictive model of itself and/or of its environment, which allows it to change state at an instant in accord with the model's predictions pertaining to a later instant". Daniel Dubois (Dubois & Resconi, 1992; Dubois, 1998a, 2000) defined the concept of incursive and hyperincursive anticipatory systems, able to generate respectively one or several anticipations influencing the computing of the next state of the system. In this article, the concept of autoincursion is proposed as the ability for a system to compute its successive internal states as a function of its past, present and anticipated states, to select among several anticipated states, and to autonomously change its own equation parameters by learning. Some fundamental properties of a neural network architecture and dynamics are proposed to define Autolncursive Memory Networks. AIM Networks can learn and activate multiple attractors simultaneously, exhibiting synergic dynamics of attractors encoding external inputs. This allows them (l) to compute their successive states as a function of past, present, and multiple anticipated states, (2) to change the way they compute their successive states through symmetric or asymmetric modification of the synaptic structure during autonomous leaming, and (3) to select sequences of anticipations oriented toward learned goals. Thu, 29 Aug 2024 15:16:38 +0200 Thu, 29 Aug 2024 15:16:50 +0200 http://popups.lib.uliege.be/1373-5411/index.php?id=2622 http://popups.lib.uliege.be/1373-5411/index.php?id=1440 A system is considered anticipatory if it has the ability to foresee the consequences of an event and act in a way it is adapted for. In order to make such judgments anticipatory systems must possess some kind of description of their surroundings, which is used in the calculation of an appropriate action. In many cases it is sufficient to have an algorithmic description to follow and some anticipatory systems do choose their actions in a completely algorithmic way. A more developed anticipatory behavior is displayed by systems, which not only possess a description but also a model of the surroundings. Those systems have an intrinsic conception of their surroundings, which they are able to reason about. This kind of anticipation is called model-based contrary to the description-based behavior, which characterizes those systems that slavishly follolw algorithmic rules. In order to take advantage of model-based behavior it is necessary to be able to properly describe the surroundings in terms of how they are perceived. Such description processes are inductive and not recursively describable. That a system can perceive and describe its own surroundings means further that it has a learning capability. Learning is the process of making order out of disorder and this is precisely the most distinguish quality of inductive inference. Genuine learning without inductive capability is impossible. The implication of this is that systems that have a model of the surroundings are not possible to implement on computers nor can computers be leaming devices contrary to what is believed in the area of machine learning. Fri, 12 Jul 2024 14:16:55 +0200 Fri, 12 Jul 2024 14:17:03 +0200 http://popups.lib.uliege.be/1373-5411/index.php?id=1440