In this paper, we examine the motivations for research on cognitive architectures and review some candidates that have been explored in the literature. After this, we consider the capabilities that a cognitive architecture should support, some properties that it should exhibit related to representation, organization, performance, and learning, and some criteria for evaluating such architectures at the systems level. In closing, we discuss some open issues that should drive future research in this important area. Key words: cognitive architectures, intelligent systems, cognitive processes 1

to be re-written?]] Abstract Replication or even modelling of consciousness in machines requires some clarifications and refinements of our concept of consciousness. Fortunately, design of, construction of, and interaction with artificial systems can itself assist in this conceptual development. In particular, if we start with the tentative hypothesis that consciousness is a form of information processing, we can build virtual machine architectures which attempt to capture various aspects of consciousness. This activity may in turn nurture the development of our concepts of consciousness in such a way that we can at last understand why consciousness is a form of information processing. This process leads gradual refinement of many of our pre-theoretical concepts of mind, showing how they can be best construed as "architecture-based" concepts.

Research on algorithms and representations once dominated AI. Recently the importance of architectures has been acknowledged, but researchers have different objectives, presuppositions and conceptual frameworks, and this, can lead to confused terminology, argumentation at cross purposes, re-invention of wheels and fragmentation of the research. We propose a methodological framework: develop a representation of a general class of architectures within which different architectures can be compared and contrasted. This should facilitate communication and integration across sub-fields of and approaches to AI, as well providing a framework for evaluating alternative architectures. As a first-draft example we present the CogAff architecture schema, and show how it provides a draft framework. But there is much still to be done.

Operating in a physical context, an intelligent robot faces two fundamental problems. First, it needs to combine information from its different sensors to form a representation of the environment that is more complete than any representation a single sensor could provide. Second, it needs to combine high-level representations (such as those for planning and dialogue) with sensory information, to ensure that the interpretations of these symbolic representations are grounded in the situated context. Previous approaches to this problem have used techniques such as (lowlevel) information fusion, ontological reasoning, and (highlevel) concept learning. This paper presents a framework in which these, and related approaches, can be used to form a shared representation of the current state of the robot in relation to its environment and other agents. Preliminary results from an implemented system are presented to illustrate how the framework supports behaviours commonly required of an intelligent robot.

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Research on algorithms and representations once dominated AI. Recently the importance of architectures has been acknowledged, but researchers have different objectives, presuppositions and conceptual frameworks, and this can lead to confused terminology, argumentation at cross purposes, re-invention of wheels and fragmentation of the research. We propose a methodological framework: develop a general representation of a wide class of architectures within which different architectures can be compared and contrasted. This should facilitate communication and integration across sub-fields of and approaches to AI, as well as providing a framework for evaluating alternative architectures. As a first-draft example we present the CogAff architecture schema, and show how it provides a useful framework for comparing and contrasting a wide range of architectures, including H-Cogaff, a proposed architecture for human-like systems. All of these concern virtual machine architectures whose natural implementations use biological mechanisms but some of which may use products of human engineering. Besides attempting to understand what sorts of virtual machine architectures are possible

Abnormal event management (AEM) in large manufacturing plants has evolved as a higher and increasingly vital function of process control. In this paper, an intelligent information management and control system is introduced. The different computational agents (i.e., modules) of the system are embodied in a three-layered cognitive hierarchy, which offers intelligent behavior at the system level, as well as at the level of specialized task agents. At the lower level, agents generate goal-seeking reactive behavior. Three different fault detection and isolation agents (i.e., three complementary techniques) are embedded to generate three different assessments and to enhance the fault isolation process. Other utility agents are also incorporated to address topics such as data reconciliation, process model identification and optimization. At the middle layer, agents enable decision making, planning, and deliberative behavior. Two case-based reasoning agents are incorporated; the first manages the system in normal operation, while the other handles faulty process situations. A meta-management agent at the highest level monitors and coordinates other agents so as to make the whole system performance more robust and coherent. 1.

The ways of modelling some of the most profound effects of emotion and arousal on cognition are discussed. Entropy reduction is used to measure quantitatively the learning speed in a cognitive model under different parameters ’ conditions. It is noticed that some settings facilitate the learning in particular stages of problem solving more than others. The entropy feedback is used to control these parameters and strategy, which in turn improves greatly the learning in the model as well as the model match with the data. This result may explain the reasons behind some of the neurobiological changes, associated with emotion and its control of the decision making strategy and behaviour. 1

Abstract. Video games in general and educational role play games in particular would increase in believability if Non Player Characters reacted appropriately to the player’s actions. Realistic and responsive feedback from game characters is important to increase engagement and enjoyment in players. In this paper, we discuss the modelling of autonomous characters based on a biologically-inspired theory of human action regulation taking into account perception, motivation, emotions, memory, learning and planning. These agents populate an educational Role Playing Game, ORIENT (Overcoming Refugee Integration with Empathic Novel Technology) dealing with the cultural-awareness problem for children aged 13 to 14. 1