... Equation, a profound insight into the nature of selection and the basis for the modern theories of kin and group selection; (ii) the theory of games and animal behavior, based on the concept of the evolutionarily stable strategy; and (iii) the modern interpretation of Fisher’s fundamental theorem of natural selection, Fisher’s theorem being perhaps the most cited and least understood idea in the history of evolutionary genetics. This paper summarizes Price’s contributions and briefly outlines why, toward the end of his painful intellectual journey, he chose to focus his deep humanistic feelings and sharp,

One of the cognitive processes responsible for social propagation is social learning, broadly meant as the process by means of which agents' acquisition of new information is caused or favoured by their being exposed to one another in a common environment. Social learning results from one or other of a number of social phenomena, the most important of which are social facilitation and imitation. In this paper, a general notion of social learning will be defined and the main processes which are responsible for it, namely social facilitation and imitation, will be analysed in terms of the social mental processes they require. A brief analysis of classical definitions of social learning is carried on, showing that a systematic and consistent treatment of this notion is still missing. A general notion of social learning is then introduced and the two main processes which may lead to it, social facilitation and imitation, will be defined as different steps on a continuum of cognitive complexity. Finally, the utility of the present approach is discussed. The analysis presented in this paper draws upon a cognitive model of social action (cf. Conte & Castelfranchi, 1995; Conte, 1999). The agent model which will be referred to throughout the paper is a cognitive model, endowed with mental properties for pursuing goals and intentions, and for knowledge-based action. To be noted, a cognitive agent is not to be necessarily meant as a natural system, although many examples examined in the paper are drawn from the real social life of humans. Cognitive agents may also be artificial systems endowed with the capacity for reasoning, planning, and decision-making about both world and mental states.

We present an architecture for autonomous creatures that allows learning to be combined with action selection, based on ideas from ethology. We show how temporal-difference learning may be used within the context of an ethologically inspired animat architecture to build and modify portions of the behavior network, and to set fundamental parameters including the strength associated with individual Releasing Mechanisms, the time course associated with appetitive behaviors, and the learning rates to be used based on the observed reliability of specific contingencies. The learning algorithm has been implemented as part of the Hamsterdam toolkit for building autonomous animated creatures. When implemented in Silas, a virtual dog, the algorithm enables Silas to be trained using classical and instrumental conditioning. 1 Introduction Action selection and learning represent two significant areas of research in behavior-based AI [Maes94], and advances in both areas are essential if we are to ...

Abstract-- In basic genetic algorithm (GA) applications, the fitness of a solution takes a value that is certain and unchanging. There are two classes of problem for which this formulation is insufficient. The first consists of ongoing searches for better solutions in a nonstationary environment, where the expected fitness of a solution changes with time in unpredictable ways. The second class consists of applications in which fitness evaluations are corrupted by noise. For problems belonging to either or both of these classes, the estimated fitness of a solution will have an associated uncertainty. Both the uncertainty due to environmental changes (process noise) and the uncertainty due to noisy evaluations (observation noise) can be reduced, at least temporarily, by re-evaluating existing solutions. The Kalman formulation provides a well-developed formal mechanism for treating uncertainty within the GA framework. It provides the mechanics for determining the estimated fitness and uncertainty when a new solution is generated and evaluated for the first time. It also provides the mechanics for updating the estimated fitness and uncertainty after an existing solution is re-evaluated, and for increasing the uncertainty with the passage of time. A Kalman-extended genetic algorithm (KGA) is developed to determine when to generate a new individual, when to re-evaluate an existing individual, and which one to re-evaluate. This KGA is applied to the problem of maintaining a network configuration with minimized message

While natural selection is often viewed as synonymous with evolution, it is widely felt to be inadequate as a theory of biological evolution; moreover, historically the concept of evolution has not been limited to biology. We propose an integrative framework for characterizing how entities evolve, in which evolution is viewed as a process of context-driven actualization of potential (CAP). Processes of change differ according to the degree of nondeterminism, and the degree to which they are sensitive to, internalize, and depend upon a particular environment or context. The approach enables us to embed phenomena across multiple disciplines into a broader conceptual framework. It suggests that the dynamical evolution of a quantum entity as described by the Schrödinger equation is not fundamentally different from change provoked by a measurement often referred to as collapse but a limiting case, with only one way to collapse. The biological transition to coded replication is seen as a means of preserving structure in the face of context, and sexual replication as a means of increasing potentiality thus enhancing diversity through interaction with context. The integrative framework sheds light on biological concepts like selection and fitness, reveals how exceptional Darwinian evolution is as a means of ‘change of state’, and clarifies in what sense culture (and the creative process underlying it) is and is not Darwinian.

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Cultural psychology, and other social sciences (e.g. cultural anthropology, sociology), seek to document cultural variation, yet have difficulty providing strong empirical tests of explanations for that variation. It is argued here that an effective means of testing hypotheses regarding the origin of, and persistence and change in, cultural variation is by simulating cultural transmission in the lab using certain methods from experimental social psychology. Three experimental methods are reviewed: the transmission chain method, the replacement method, and the constant-group method. Although very few studies have explicitly simulated specific cross-cultural patterns, much potential exists for future investigations. This integration of small-scale experimental simulations and largescale observational or historical data is facilitated by an evolutionary framework for the study of culture, and has a precedent in the biological sciences, where experiments are used to simulate and explain the processes of biological evolution.

Engineers have traditionally been trained to solve design problems without regard to the potentially competing interests of other designers. But just as technology strategists are increasingly drawn into the technical minutiae of product development decisions, engineers are increasingly exposed to the competitive forces that shape their requirements and the resources at their disposal. I propose that architectural strategy—the application of strategic thinking to system design problems—should therefore be integrated with the theory and practice of engineering design. This task is made difficult by the fact that system architects and business strategists focus on problems at different levels of abstraction that lend themselves to different kinds of tools. My dissertation research bridges this gap in three ways. First, I develop a representation scheme, based on design structure networks, that allows economic relationships among system components to be expressed in a graphical language that is both visually intuitive and formally rigorous. Second, I model the evolution of designs as a sequence of “moves ” by designers that see and seek economic value. Although these system

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Abstract: Agile software development methods mainly aim at increasing software quality by fostering customer collaboration and performing exhaustive testing. The introduction of Extreme Programming (XP) – the most common agile software development method – into an organization is accompanied with conceptual and organizational changes. These changes range from daily-life changes (e.g., sitting together and maintaining an informative project environment) and continue with changes on the management level (e.g., meeting and listening to the customer during the whole process and the concept of the whole team which means that all role holders are part of the team). This paper examines the process of transition to an agile development process in a large-scale software project in the Israeli Air Force as it is perceived from the system analysis and design perspective. Specifically, the project specifications of the agile team are compared with those of a team who continues working according to the previous heavyweight method during the first half year of transition. Size and complexity measures are used as the basis of the comparison. In addition to the inspection of the specifications, the change in the role of the system analysts, as the system analysts conceive of it, is examined. 1