On relating functional modeling approaches: abstracting functional models from behavioral models

This paper presents a survey of functional modeling approaches and describes a strategy to establish functional knowledge exchange between them. This survey is focused on a comparison of function meanings and representations. It is argued that functions represented as input-output flow transformations correspond to behaviors in the approaches that characterize functions as intended behaviors. Based on this result a strategy is presented to relate the different meanings of function between the approaches, establishing functional knowledge exchange between them. It is shown that this strategy is able to preserve more functional information than the functional knowledge exchange methodology of Kitamura, Mizoguchi, and co-workers. The strategy proposed here consists of two steps. In step one, operation-on-flow functions are translated into behaviors. In step two, intended behavior functions are derived from behaviors. The two-step strategy and its benefits are demonstrated by relating functional models of a power screwdriver between methodologies

Why it is important to build robots capable of doing science

Science, like any other cognitive activity, is grounded in the sensorimotor interaction of our bodies with the environment. Human embodiment thus constrains the class of scientific concepts and theories which are accessible to us. The paper explores the possibility of doing science with artificial cognitive agents, in the framework of an interactivist-constructivist cognitive model of science. Intelligent robots, by virtue of having different sensorimotor capabilities, may overcome the fundamental limitations of human science and provide important technological innovations. Mathematics and nanophysics are prime candidates for being studied by artificial scientists

Evolutionary Robotics: a new scientific tool for studying cognition

We survey developments in Artificial Neural Networks, in Behaviour-based Robotics and Evolutionary Algorithms that set the stage for Evolutionary Robotics in the 1990s. We examine the motivations for using ER as a scientific tool for studying minimal models of cognition, with the advantage of being capable of generating integrated sensorimotor systems with minimal (or controllable) prejudices. These systems must act as a whole in close coupling with their environments which is an essential aspect of real cognition that is often either bypassed or modelled poorly in other disciplines. We demonstrate with three example studies: homeostasis under visual inversion; the origins of learning; and the ontogenetic acquisition of entrainment

Can the g Factor Play a Role in Artificial General Intelligence Research?

In recent years, a trend in AI research has started to pursue human-level, general artificial intelli-gence (AGI). Although the AGI framework is characterised by different viewpoints on what intelligence is and how to implement it in artificial systems, it conceptualises intelligence as flexible, general-purposed, and capable of self-adapting to different contexts and tasks. Two important ques-tions remain open: a) should AGI projects simu-late the biological, neural, and cognitive mecha-nisms realising the human intelligent behaviour? and b) what is the relationship, if any, between the concept of general intelligence adopted by AGI and that adopted by psychometricians, i.e., the g factor? In this paper, we address these ques-tions and invite researchers in AI to open a dis-cussion on the theoretical conceptions and practi-cal purposes of the AGI approach

Telling the difference between deceiving and truth telling: An experiment in a public space

The behavioral experiment presented in this paper investigated deception tasks (both concealment and lying) undertaken in a public space. The degree of risk of deception detection and the demands of self-regulation when deceiving were manipulated. The results showed a significant interaction effect between veracity and risk of deception detection, emerged for the body movement of “hand(s) in pocket(s)”. The incidence of “hand(s) in pocket(s)” was found to increase from truth telling to deceiving conditions when the risk of deception detection was higher, and to decrease from truth telling to deceiving conditions when the risk was lower. Higher risk of deception detection was also found in magnifying the “overall negative and controlled impression” displayed by both deceivers and truth tellers, compared to the lower risk of detection condition. We also discussed the possible effects of risk of deception detection and depletion of self-regulation, on deception behavior. Further studies and the connection between this study and the research community of computer vision and multimodel interaction is also discussed

Formulating Consciousness: A Comparative Analysis of Searle’s and Dennett’s Theory of Consciousness

This research will argue about which theory of mind between Searle’s and Dennett’s can better explain human consciousness. Initially, distinctions between dualism and materialism will be discussed ranging from substance dualism, property dualism, physicalism, and functionalism. In this part, the main issue that is tackled in various theories of mind is revealed. It is the missing connection between input stimulus (neuronal reactions) and behavioral disposition: consciousness. Then, the discussion will be more specific on Searle’s biological naturalism and Dennett’s multiple drafts model as the two attempted to answer the issue. The differences between them will be highlighted and will be analyzed according to their relation to their roots: dualism and materialism. The two theories will be examined on how each answer the questions on consciousness

Cooperation, Norms, and Revolutions: A Unified Game-Theoretical Approach

Cooperation is of utmost importance to society as a whole, but is often challenged by individual self-interests. While game theory has studied this problem extensively, there is little work on interactions within and across groups with different preferences or beliefs. Yet, people from different social or cultural backgrounds often meet and interact. This can yield conflict, since behavior that is considered cooperative by one population might be perceived as non-cooperative from the viewpoint of another. To understand the dynamics and outcome of the competitive interactions within and between groups, we study game-dynamical replicator equations for multiple populations with incompatible interests and different power (be this due to different population sizes, material resources, social capital, or other factors). These equations allow us to address various important questions: For example, can cooperation in the prisoner's dilemma be promoted, when two interacting groups have different preferences? Under what conditions can costly punishment, or other mechanisms, foster the evolution of norms? When does cooperation fail, leading to antagonistic behavior, conflict, or even revolutions? And what incentives are needed to reach peaceful agreements between groups with conflicting interests? Our detailed quantitative analysis reveals a large variety of interesting results, which are relevant for society, law and economics, and have implications for the evolution of language and culture as well