Brauchen Roboter moralische Kompetenz? Die Antwort lautet ja. Einerseits benötigen Roboter moralische Kompetenz, um unsere Welt aus Regeln, Vorschriften und Werten zu begreifen, andererseits um von ihrem Umfeld akzeptiert zu werden. Wie aber lässt sich moralische Kompetenz in Roboter implementieren? Welche philosophischen Herausforderungen sind zu erwarten? Und wie können wir uns und unsere Kinder auf Roboter vorbereiten, die irgendwann über moralische Kompetenz verfügen werden? André und Iga Maria Schmiljun skizzieren aus einer humanistisch-philosophischen Perspektive erste Antworten auf diese Fragen und entwickeln (...) einen didaktischen Einstieg in das Thema für die Grundschule. (shrink)

For over a decade neuroscience has uncovered that appropriate decision-making in daily life decisions results from a strong interplay between cognition and covert biases produced by emotional processes. This interplay is particularly important in social contexts: lesions in the pathways supporting these processes provoke serious impairments on social behavior. One important mechanism in social contexts is empathy, fundamental for appropriate social behavior. This paper presents arguments supporting this connection between cognition and emotion, in individual as well as in social contexts. (...) The central claim of this paper is that biologically inspired cognitive architectures ought to include these mechanisms. A taxonomy of computational models addressing emotions is presented, together with a brief survey of the research published in this area. The Prisoner Dilemma game is used as a case study exposing the trade-off between individual rationality and cooperative behavior. Experiments using a simple implementation of empathy and emotion expression, employing an Iterated Prisoner Dilemma setup, illustrate the emergence of a cooperative behavior mutually beneficial for both players. (shrink)

Abstract In this paper, I argue that the traditional philosophical approach of taking cognitively and emotionally competent adult people to be the prototypical instances of agency should be revised in light of current work in the behavioral sciences. Logical consistency in application is better served by taking simple goal-directed and feedback-governed systems such as insects as the prototypes of the concept of agency, with people being agents ?by extension? in the same sense as countries or corporations.

Chomskyian claims of a genetically hard-wired and cognitively autonomous “universal grammar” are being promoted by generative linguistics as facts about language to the present day. The related doctrine of an evolutionary discontinuity in language emergence, however, is based on misconceptions about the notions of homology and preadaptation. The obvious lack of equivalence between symbolic communicative capacities in existing nonhuman primates and human language does not preclude common roots. Normal and disordered language development is strongly influenced by the genome, but there (...) is no evidence for the existence of specific genes underlying “universal grammar.” In the mature brain, stages of language processing can be distinguished and “first-pass” syntactic analyses appear to precede semantic decoding. However, this partial seriality – as well as behavioral and clinical dissociations between lexical and functional categories – can be best described, not in terms of serially activated and discrete modules, but in terms of classes of cell assemblies that differ in their distributional properties. Whereas cell assemblies involved in semantic interpretation (“content word” assemblies) are widely distributed and are generally less vulnerable to focal lesion, those involved in structural decoding (functor assemblies) are primarily distributed within the left perisylvian cortices and are selectively vulnerable to left perisylvian lesion. These distributional differences are explained in terms of the perceptuomotor components involved in the acquisition of relevant representations. The emerging “motivated” or “toposemantic” brain regional specificity can only be accommodated with “soft” and maturational versions of modularity. The failure to reproduce double dissociations in current connectionist models is due to overly simple neuroscientific assumptions, notably that of overall equipotentiality. Linguistic models should not be expected to be “implemented” in the brain, but need to be constrained by neuroscientific evidence on how biological brains function. (shrink)

Contrary to Müller's claims, and in support of modular theories, genetic factors play a substantial and significant role in language. The finding that some children with specific language impairment (SLI) have nonlinguistic impairments may reflect improper diagnosis of SLI or impairments that are secondary to linguistic impairments. Thus, such findings do not argue against the modularity thesis. The lexical/functional distinction appears to be innate and specifically linguistic and could be instantiated in either symbolic or connectionist systems.

The belief that syntax is an innate, autonomous, species-specific module is highly questionable. Syntax demonstrates the mosaic nature of evolutionary change, in that it made use of (and led to the enhancement of) numerous preexisting neurocognitive features. It is best understood as an emergent characteristic of the explosion of semantic complexity that occurred during hominid evolution.

Müller's target article aims to summarize approaches to the question of how language elements (phonemes, morphemes, etc.) and rules are laid down in the brain. However, it suffers from being too vague about basic assumptions and empirical predictions of neurobiological models, and the empirical evidence available to test the models is not appropriately evaluated. (1) In a neuroscientific model of language, different cortical localizations of words can only be based on biological principles. These need to be made explicit. (2) Evidence (...) for and against word class differences could be evaluated more rigorously. (3) All (and only) humans are able to learn languages with complex syntactic structures; it is, therefore, not appropriate to deny innateness and universality of syntactic principles. The real question appears to be the following: Which neurobiological principles are the linguistic principles based on? (shrink)

I enlarge on the theme that the brain mechanisms required for languageand other aspects of the human mind evolved through selective changes in the regulatory genes governing growth. Extension of the period of postnatal growth increases the role of the environment in structuring the brain, and spatiotemporal programming (heterochrony) ofgrowth might explain hierarchical representation, hemispheric specialization, and perhaps sex differences.

The concepts of the innateness, universality, species-specificity, and autonomy of the human language capacity have had an extreme impact on the psycholinguistic debate for over thirty years. These concepts are evaluated from several neurobiological perspectives, with an emphasis on the emergence of language and its decay due to brain lesion and progressive brain disease.Evidence of perceptuomotor homologies and preadaptations for human language in nonhuman primates suggests a gradual emergence of language during hominid evolution. Regarding ontogeny, the innate component of language (...) capacity is likely to be polygenic and shared with other developmental domains. Dissociations between verbal and nonverbal development are probably rooted in the perceptuomotor specializations of neural substrates rather than the autonomy of a grammar module. Aphasiologicaldata often assumed to suggest modular linguistic subsystems can be accounted for in terms of a neurofunctional model incorporating perceptuomotor-based regional specializationsand distributivity of representations. Thus, dissociations between grammatical functors and content words are due to different conditions of acquisition and resulting differences in neural representation. Human brains are characterized by multifactorial interindividual variability, and strict universality of functional organization is biologically unrealistic.A theoretical alternative is proposed according to which (1) linguistic specialization of brain areas is due to epigenetic and probabilistic maturational events, not to genetic ”hard-wiring,” and (2) linguistic knowledge is neurally represented in distributed cell assemblies whose topography reflects the perceptuomotor modalities involved in the acquisition and use of a given item of knowledge. (shrink)

Artificial intelligence (AI) and robotics are digital technologies that will have significant impact on the development of humanity in the near future. They have raised fundamental questions about what we should do with these systems, what the systems themselves should do, what risks they involve, and how we can control these. - After the Introduction to the field (§1), the main themes (§2) of this article are: Ethical issues that arise with AI systems as objects, i.e., tools made and used (...) by humans. This includes issues of privacy (§2.1) and manipulation (§2.2), opacity (§2.3) and bias (§2.4), human-robot interaction (§2.5), employment (§2.6), and the effects of autonomy (§2.7). Then AI systems as subjects, i.e., ethics for the AI systems themselves in machine ethics (§2.8) and artificial moral agency (§2.9). Finally, the problem of a possible future AI superintelligence leading to a “singularity” (§2.10). We close with a remark on the vision of AI (§3). - For each section within these themes, we provide a general explanation of the ethical issues, outline existing positions and arguments, then analyse how these play out with current technologies and finally, what policy consequences may be drawn. (shrink)

Book Description (Blurb): Cognitive Design for Artificial Minds explains the crucial role that human cognition research plays in the design and realization of artificial intelligence systems, illustrating the steps necessary for the design of artificial models of cognition. It bridges the gap between the theoretical, experimental and technological issues addressed in the context of AI of cognitive inspiration and computational cognitive science. -/- Beginning with an overview of the historical, methodological and technical issues in the field of Cognitively-Inspired Artificial Intelligence, (...) Lieto illustrates how the cognitive design approach has an important role to play in the development of intelligent AI technologies and plausible computational models of cognition. Introducing a unique perspective that draws upon Cybernetics and early AI principles, Lieto emphasizes the need for an equivalence between cognitive processes and implemented AI procedures, in order to realise biologically and cognitively inspired artificial minds. He also introduces the Minimal Cognitive Grid, a pragmatic method to rank the different degrees of biologically and cognitive accuracy of artificial systems in order project and predict their explanatory power with respect to the natural systems taken as source of inspiration. -/- Providing a comprehensive overview of cognitive design principles in constructing artificial minds, this text will be essential reading for students and researchers of artificial intelligence and cognitive science. (shrink)

In this paper, I explore some of the ways in which Artificial Intelligence (AI) is mediated discursively. I assume that AI is informed by an “ancestral dream” to reproduce nature by artificial means. This dream drives the production of “cyborg discourse”, which hinges on the belief that human nature (especially intelligence) can be reduced to symbol manipulation and hence replicated in a machine. Cyborg discourse, I suggest, produces AI systems by rhetorical means; it does not merely describe AI systems or (...) reflect a set of prevailing attitudes about technology. To support this argument, I analyse a set of research articles about an “embodied conversational agent” called the Real Estate Agent (REA). The articles about REA mobilise a set of rhetorical strategies that systematically downplay the system’s artificiality and bolster its humanlike qualities. Within the context of the dream of AI to produce humanlike machines, and given our strong bias for human-human interaction, the designers’ claim to REA’s humanness in their research articles, as I argue in the final section of this paper, needs little justification. (shrink)

Most research in the natural sciences passes through repeated cycles of a analytic reduction to the next lower level of organization, then resynthesis to the original level, then new analyticareduction, and so on. A residue of unexplained phenomena at the original level appears at first to require a holistic description independent of the lower level, but the residue shrinks as knowledge increases.This principle is well illustrated by recent studies from the social organization of insects, several examples of which are cited (...) here. In theory it should also apply to human social organization. Culture is biological: meaning in culture can be approached as the outcome of mechanism-based causation, because culture stems from individual cognition, which has a biological basis. It would seem to follow that the most effective way to study culture is across all levels of organization from gene to society, passing repetitively through a cycle of reduction and synthesis in the manner of the natural sciences. Reductionistic analysis is favored by the tendency of semantic memory and culture to occur in discrete units that are arranged hierarchically. (shrink)

The conventional approach to developing expert systems views the domain of application as being “formally defined”. This view often leads to practical problems when expert systems are built using this approach. This paper examines the implications and problems of the formal approach to expert system design and proposes an alternative approach based on the concept of semi-formal domains. This approach, which draws on the work of socio-technical information systems, provides guidelines which can be used for the design of successful expert (...) systems. (shrink)

Recent discussions in cognitive science and the philosophy of mind have defended a theory according to which we live in a virtual world akin to a computer simulation, generated by our brain. It is argued that our brain creates a model world from a variety of stimuli; this model is perceived as if it was external and perception-independent, even though it is neither of the two. The view of the mind, brain, and world, entailed by this theory has some peculiar (...) consequences which have rarely been explored in detail. This paper sets out virtual world theory and relates it to various central philosophical problems, the role of the perceiver and the problem of the existence of the external world ). The second part suggests three interpretations of virtual world theory, two familiar ones and a somewhat less familiar one. The remainder of the paper argues that the irrealist interpretation is the one we should adopt. (shrink)

Recently, there has been a resurgence of interest in general, comprehensive models of human cognition. Such models aim to explain higher-order cognitive faculties, such as deliberation and planning. Given a computational representation, the validity of these models can be tested in computer simulations such as software agents or embodied robots. The push to implement computational models of this kind has created the field of artificial general intelligence (AGI). Moral decision making is arguably one of the most challenging tasks for computational (...) approaches to higher-order cognition. The need for increasingly autonomous artificial agents to factor moral considerations into their choices and actions has given rise to another new field of inquiry variously known as Machine Morality, Machine Ethics, Roboethics, or Friendly AI. In this study, we discuss how LIDA, an AGI model of human cognition, can be adapted to model both affective and rational features of moral decision making. Using the LIDA model, we will demonstrate how moral decisions can be made in many domains using the same mechanisms that enable general decision making. Comprehensive models of human cognition typically aim for compatibility with recent research in the cognitive and neural sciences. Global workspace theory, proposed by the neuropsychologist Bernard Baars (1988), is a highly regarded model of human cognition that is currently being computationally instantiated in several software implementations. LIDA (Franklin, Baars, Ramamurthy, & Ventura, 2005) is one such computational implementation. LIDA is both a set of computational tools and an underlying model of human cognition, which provides mechanisms that are capable of explaining how an agent’s selection of its next action arises from bottom-up collection of sensory data and top-down processes for making sense of its current situation. We will describe how the LIDA model helps integrate emotions into the human decision-making process, and we will elucidate a process whereby an agent can work through an ethical problem to reach a solution that takes account of ethically relevant factors. (shrink)

One can disagree with Müller that it is neurobiologically questionable to suppose that human language is innate, specialized, and species-specific, yet agree that the precise brain mechanisms controlling language in any individual will be influenced by epigenesis and genetic variability, and that the interplay between inherited and acquired aspects of linguistic capacity deserves to be investigated.

In this philosophical paper, we explore computational and biological analogies to address the fine-tuning problem in cosmology. We first clarify what it means for physical constants or initial conditions to be fine-tuned. We review important distinctions such as the dimensionless and dimensional physical constants, and the classification of constants proposed by Lévy-Leblond. Then we explore how two great analogies, computational and biological, can give new insights into our problem. This paper includes a preliminary study to examine the two analogies. Importantly, (...) analogies are both useful and fundamental cognitive tools, but can also be misused or misinterpreted. The idea that our universe might be modelled as a computational entity is analysed, and we discuss the distinction between physical laws and initial conditions using algorithmic information theory. Smolin introduced the theory of "cosmological natural selection" with a biological analogy in mind. We examine an extension of. (shrink)

For a long time, emotions have been ignored in the attempt to model intelligent behavior. However, within the last years, evidence has come from neuroscience that emotions are an important facet of intelligent behavior being involved into cognitive problem solving, decision making, the establishment of social behavior, and even conscious experience. Also in research communities like software agents and robotics, an increasing number of researchers start to believe that computational models of emotions will be needed to design intelligent systems. Nevertheless, (...) modeling emotions in technical terms poses many difficulties and has often been accounted as just not feasible. In this article, there are identified the main problems, which occur when attempting to implement emotions into machines. By pointing out these problems, it is aimed to avoid repeating mistakes committed when modeling computational models of emotions in order to speed up future development in this area. The identified issues are not derived from abstract reflections about this topic but from the actual attempt to implement emotions into a technical system based on neuroscientific research findings. It is argued that besides focusing on the cognitive aspects of emotions, a consideration of the bodily aspects of emotions—their grounding into a visceral body—is of crucial importance, especially when a system shall be able to learn correlations between environmental objects and events and their “emotional meaning”. (shrink)

In the visual system, the represented features of individual objects (shape, colour, movement, and so on) are distributed both in space and time within the brain. Representations of inner and outer event sequences arrive through different sense organs at different times, and are likewise distributed. Objects are nevertheless perceived as integrated wholes - and event sequences are experienced to form a coherent "consciousness stream." In their thoughtful article, Dennett & Kinsbourne ask how this is achieved.

The broad spectrum revolution brought greater dependence on skill and knowledge, and more demanding, often social, choices. We adopt Sterelny's account of how cooperative foraging paid the costs associated with longer dependency, and transformed the problem of skill learning. Scaffolded learning can facilitate cognitive control including suppression, whereas scaffolded exchange and trade, including inter-temporal exchange, can help develop resolve.

Consciousness is seen as a difficult “binding” problem. Binding, a process where different sensations evoked by an item are associated in the nervous system, can be viewed as a process similar to associative learning. Several reports that consciousness is associated with some form of memory imply that different forms of memories have a common feature contributing to consciousness. Based on a proposed synaptic mechanism capable of explaining different forms of memory, we developed a framework for consciousness. It is based on (...) the formation of semblance of sensory stimulus from (1) synaptic semblances when excitatory postsynaptic potentials arrive at functionally LINKed postsynaptic membranes, and (2) network semblances when these potentials summate to elicit action potential initiating activity in a network of neurons. It is then possible to derive a framework for consciousness as a multi-dimensional semblance. According to this framework, a continuum of semblances formed from background sensory stimuli and oscillating neuronal activities serve to maintain consciousness. Feasibility of this framework to explain various physiological and pathological states of consciousness, its subjective nature and qualia is examined. (shrink)

In the nineteenth century, there was substantial and sophisticated interest in neuroscience on the part of social theorists, including Comte and Spencer, and later Simon Patten and Charles Ellwood. This body of thinking faced a dead end: it could do little more than identify highly general mechanisms, and could not provide accounts of such questions as `why was there no proletarian revolution?' Psychologically dubious explanations, relying on neo-Kantian views of the mind, replaced them. With the rise of neuroscience, however, some (...) of the problems of concern to earlier thinkers, such as imitation, have revived because of the discovery of neuronal mechanisms, or through fMRI studies. The article reviews the history and discusses the implications of current work for the reconsideration of traditional social theory concepts. It is suggested that certain kinds of bridging work with neuroscience would enable us to answer many questions in social theory that empirical sociology has failed to answer. (shrink)

For my part, my only hope is, that I may contribute a little to the advancement of knowledge, by giving in some particulars a different turn to the speculations of philosophers, and pointing out to them more distinctly those subjects, where alone they can expect assurance and conviction.

The broad range of capabilities exhibited by humans and animals is achieved through a large set of heterogeneous, tightly integrated cognitive mechanisms. To move artificial systems closer to such general-purpose intelligence we cannot avoid replicating some subset—quite possibly a substantial portion—of this large set. Progress in this direction requires that systems integration be taken more seriously as a fundamental research problem. In this paper I make the argument that intelligence must be studied holistically. I present key issues that must be (...) addressed in the area of integration and propose solutions for speeding up rate of progress towards more powerful, integrated A.I. systems, including (a) tools for building large, complex architectures, (b) a design methodology for building realtime A.I. systems and (c) methods for facilitating code sharing at the community level. (shrink)

Can theories of mental imagery, conscious mental contents, developed within cognitive science throw light on the obscure (but culturally very significant) concept of imagination? Three extant views of mental imagery are considered: quasi‐pictorial, description, and perceptual activity theories. The first two face serious theoretical and empirical difficulties. The third is (for historically contingent reasons) little known, theoretically underdeveloped, and empirically untried, but has real explanatory potential. It rejects the “traditional” symbolic computational view of mental contents, but is compatible with recentsituated (...) cognition and active visionapproaches in robotics. This theory is developed and elucidated. Three related key aspects of imagination (non‐discursiveness, creativity, and seeing as) raise difficulties for the other theories. Perceptual activity theory presents imagery as non‐discursive and relates it closely to seeing as. It is thus well placed to be the basis for a general theory of imagination and its role in creative thought. (shrink)

There are two classical and opposite positions about scientific discovery: the one that conceives scientific discovery activity as fully rational and the one that conceives scientific discovery activity as fully irrational. In the first case, machines are regarded as able to perform the scientific discovery process whereas, in the second case, machines are considered unable to perform any part of the scientific discovery process.We adopt a third intermediate approach that envisages a new role for machines, which are conceived as descriptions (...) of the results of scientific discovery activity. More precisely, the purpose of the paper is to illustrate the multilevel structure of a machine, called creative dynamic agency, that represents the articulated and incremental description of the product of scientific discovery process. The multilevel architecture reflects the composition relation that holds among phenomena described by creative agents that compose creative dynamic agency. (shrink)

Evidence is presented from a polyglot savant to suggest that double dissociations between linguistic and nonverbal abilities are more important than Müller's target article implies. It is also argued that the special nature of syntax makes its assimilation to other aspects of language or to nonhuman communication systems radically implausible.