Approximate planning in spatial search.

Hazardous waste capacity assurance planning: Volumes for one‐time remediation

Incremental Conceptualization for Language Production discusses the simultaneous actions involved in thinking and speaking, as well as the piecemeal way in which individuals construct an internal representation of the external world and use this internal representation for speaking. Author Markus Guhe presents the first computational model that captures these observations in a cognitively adequate fashion. The volume is an innovative look at the mind’s process of producing semantic representations that can be transformed into language. The first section of the book illustrates four stages of conceptualization: construction of a conceptual representation; selection of content to be verbalized; linearization of the selected content; and generation of preverbal messages. Guhe then analyzes incremental processing — processing that takes place in a piecemeal fashion — and offers a blueprint of incremental models while discussing the dimensions along which the processing principles and the blueprint varies. Finally, incremental processing and conceptualization merge to form the incremental conceptualiser model (inC). The effective use of inC is demonstrated through simulations carried out with the implementation of the model. Intended for researchers in cognitive science, particularly cognitive modeling of language, this volume will also interest researchers in artificial intelligence, computational linguistics, psycholinguistics, and linguistics and psychology.

Enactivism has had an important conceptual influence on Artificial Life. Its notorious mind-life continuity thesis is a fundamental idea that once sustained much of the theoretical framework, but has recently lost support, explicitly in cognitive science, and by omission in Artificial Life. Alternative computational accounts, however, do not seem capable of filling the vacuum it leaves behind, given that according to the Church-Turing thesis, the limit of what is computable is bounded by Turing machines (regardless of the specific instantiation, such as cellular automata, deep, spiking or any other form of neural networks, or even quantum computers, to name some well known cases) and that computable functions formally describe the notion of mechanism, every organismic process that determines a consistent response should be both limited to Turing-machine capabilities and amenable to formalization. This impending issue, namely, the relation among life, cognition and computation as the hypothetical elements of the mind, is particularly pertinent and highly relevant for Artificial Life. Moreover, although it is often overlooked or omitted for practical reasons, it will probably define, or so we argue, the direction of future research in cognitive science, artificial intelligence and related fields.

In recent years, the topic of emotion reading has increasingly received attention from researchers in Cognitive Science and Artificial Intelligence. To study this phenomenon, in this paper an adaptive agent model is presented with capabilities to interpret another agent’s emotions. The presented agent model is based on recent advances in neurological context. First a non-adaptive agent model for emotion reading is described involving (preparatory) mirroring body states of the other agent. Here emotion reading is modelled taking into account the Simulation Theory perspective as known from the literature, involving the own body states and emotions in reading somebody else’s emotions. This models an agent that first develops the same feeling, and after feeling the emotion imputes it to the other agent. Next the agent model is extended to an adaptive model based on a Hebbian learning principle to develop a direct connection between a sensed stimulus concerning another agent’s body state (e.g., face expression) and the emotion recognition state. In this adaptive agent model the emotion is imputed to the other agent before it is actually felt. The agent model has been designed based on principles of neural modelling, and as such has a close relation to a neurological realisation.Keywordsagent modelemotion readingcognitivetheory of mindadaptive

The advancements of the research on Mobile Robots with high degree of autonomy is possible, on one hand, due to its broad perspective of applications and, on other hand, due to the development and reduction of costs on computer, electronic and mechanic systems. Together with the research in Artificial Intelligence and Cognitive Science, this scenario currently enables the proposition of ambitious and complex robotic projects. Most of the applications were developed outside the structured environment of industry assembly lines and have complex goals, such as planets exploration, transportation of parts in factories, manufacturing, cleaning and monitoring of households, handling of radioactive materials in nuclear power plants, inspection of volcanoes, and many other activities. This chapter presents and discusses the main topics involved on the design or adoption of a mobile robot system and focus on the control and navigation systems for autonomous mobile robots. Thus, this chapter is organized as follows: • The Section 2 introduces the main aspects of the Robot design, such as: the conceptualization of the mobile robot physical structure and its relation to the world; the state of art of navigation methods and systems; and the control architectures which enables high degree of autonomy. • The Section 3 presents the dynamic and control analysis for navigation robots with kinematic and dynamic model of the differential and omnidirectional robots. • And finally, Section 4 presents applications for a robotic platform of Automation, Simulation, Control and Supervision of Navegation Robots, with studies of dynamic and kinematic modeling, control algorithms, mechanisms for mapping and localization, trajectory planning and the platform simulator.

The Interslip-D project was formed to develop a personal machine inplementation of Interlisp for use as an environment for research in artificial intelligence and cognitive science [Burton et al., 80b]. This note describes the principal developments since our last report almost a year ago [Burton et al., 80a].

Research in artificial intelli-gence, artificial life and cogni-tive science has not yet provided answers to any of the most perplexing questions about the mind, such as the nature of consciousness or of the self; in this article the authors make a suggestion for a new approach. They begin by setting their project in the broader cognitive science context and argue that little recent research adequately addresses the question of what are the necessary requirements for conscious experience to be possible. Kant addresses this question in his transcendental psychology, and although Kant's work is now over 200 years old the authors believe his approach is worthy of re-examination in the current debate about the mind.

In his book “Minds and Computers: An Introduction to the Philosophy of Artificial Intelligence”, Matt Carter presents a comprehensive exploration of the philosophical questions surrounding artificial intelligence (AI). Carter argues that the development of AI is not merely a technological challenge but fundamentally a philosophical one. He delves into key issues like the nature of mental states, the limits of introspection, the implications of memory decay, and the functionalist framework that allows for the possibility of AI. Carter contrasts functionalism with reductive materialism, highlighting how the former accommodates the concept of artificial minds. He also emphasizes the significance of computationalism, which combines functionalism with computational theory to provide a robust explanation for mental processes. The book further discusses formal systems, the role of register machines in computation, and the inherent challenges in AI research, particularly in developing natural languag processing systems. Carter’s work underscores the limitations of current computational models of cognition while remaining hopeful that advances in neuroscience may lead to stronger AI systems in the future. This book provides critical insights for researchers in AI and cognitive science, motivating further inquiry into the philosophical foundations of artificial intelligence and its practical implications.

Building a human-like integrative artificial cognitive system, that is, an artificial general intelligence (AGI), is the holy grail of the artificial intelligence (AI) field. Furthermore, a computational model that enables an artificial system to achieve cognitive development will be an excellent reference for brain and cognitive science. This paper describes an approach to develop a cognitive architecture by integrating elemental cognitive modules to enable the training of the modules as a whole. This approach is based on two ideas: (1) brain-inspired AI, learning human brain architecture to build human-level intelligence, and (2) a probabilistic generative model (PGM)-based cognitive architecture to develop a cognitive system for developmental robots by integrating PGMs. The proposed development framework is called a whole brain PGM (WB-PGM), which differs fundamentally from existing cognitive architectures in that it can learn continuously through a system based on sensory-motor information.In this paper, we describe the rationale for WB-PGM, the current status of PGM-based elemental cognitive modules, their relationship with the human brain, the approach to the integration of the cognitive modules, and future challenges. Our findings can serve as a reference for brain studies. As PGMs describe explicit informational relationships between variables, WB-PGM provides interpretable guidance from computational sciences to brain science. By providing such information, researchers in neuroscience can provide feedback to researchers in AI and robotics on what the current models lack with reference to the brain. Further, it can facilitate collaboration among researchers in neuro-cognitive sciences as well as AI and robotics.

There is increasingly much agreement in the cognitive sciences that human cognition is embodied – to some significant extent. However, there is much less agreement regarding in what sense(s) cognition is embodied. In particular, there is much agreement that sensorimotor interaction with the environment is fundamental to cognition. From a historical perspective, this emphasis on the sensorimotor body is at least partly due to the crucial role that the conceptual shift in artificial intelligence (AI) research – from computational to robotic models – has played in the overall development of embodied cognitive science. Most embodied AI research, however, in particular work on symbol grounding and related approaches, reduces the body to a mere sensorimotor interface for internal processes that are still just as computational as they were 30–40 years ago. In Harnad's terms, this type of AI has only gone from a computational to a robotic functionalism. In theory, this could be limited to AI research as such, but in practice the view of the physical body as the computational mind's sensorimotor interface to the world still pervades much of cognitive science and philosophy of mind. The argument presented here is that there are good reasons to say that at least today's robots are in fact not embodied – in any sense that would allow for anything even close to human embodied cognition and intentionality – and that this has implications for social interactions between humans and robots.

Distributional semantics develops theories and methods to represent the meaning of natural language expressions, with vectors encoding their statistical distribution in linguistic contexts. It is at once a theoretical model to express meaning, a practical methodology to construct semantic representations, a computational framework for acquiring meaning from language data, and a cognitive hypothesis about the role of language usage in shaping meaning. This book aims to build a common understanding of the theoretical and methodological foundations of distributional semantics. Beginning with its historical origins, the text exemplifies how the distributional approach is implemented in distributional semantic models. The main types of computational models, including modern deep learning ones, are described and evaluated, demonstrating how various types of semantic issues are addressed by those models. Open problems and challenges are also analyzed. Students and researchers in natural language processing, artificial intelligence, and cognitive science will appreciate this book.

Suffering the Modernist Legacy of Husserlian Phenomenology James J. Hodge (bio) Testing the Limit: Derrida, Levinas, Henry, and the Phenomenological Tradition by François-David Sebbah. Palo Alto, CA: Stanford University Press, 2012. Pp. 336. $90.00 cloth, $29.95 paper. Phenomenology has received a curiously spotty reception in the wake of the influx of Continental philosophy in language departments in North America beginning in the 1960s. Of the three major philosophers in the phenomenological tradition, Martin Heidegger remains the most widely read, yet he is also regularly discussed as though somehow beyond the context of phenomenology. Maurice Merleau-Ponty's death in 1961 deprived him of the opportunity to engage the emerging Anglo-American appetite for French theory in person and in "the flesh." More significantly, it denied him the chance of responding to the casual statements levied against phenomenology in the works of Gilles Deleuze, Michel Foucault, and other stars of the '68 generation. And although his voluminous and antiseptic prose has undoubtedly turned off many potential readers, Edmund Husserl's legacy has suffered from the errant impression that Jacques Derrida's most famous works from 1967—including Of Grammatology and especially Voice and Phenomenon—constitute something of a summary rejection of Husserl's project insofar as it represents the grand villain of the "metaphysics of presence." Husserl's reputation—again, within language departments and not necessarily philosophy departments—has suffered in inverse correlation with Derrida's status [End Page 155] as perhaps the most influential and famous avatar of so-called "French theory." This situation has always been quite unfair to Husserl and phenomenology more generally. With the rise of interest in the body, embodiment, and affect in the last two decades or so, the situation has started to change. Phenomenology has begun to achieve more critical currency. Merleau-Ponty's work has proven particularly important for the work of several influential scholars in the fields of film studies and new media studies. Beyond the work of Vivian Sobchack and Mark B. N. Hansen, however, the word phenomenology has largely become disconnected from its philosophical origins.1 Use of the word phenomenological as a synonym for embodied experience constitutes merely the most egregious symptom of this condition. Discussion of Husserl's work, it almost goes without saying, remains marginal at best, and the very meaning of phenomenology remains unproductively elusive. Although it is not aimed at correcting these problems of North American literary and media studies, Stephen Barker's translation of François-David Sebbah's 2001 monograph Testing the Limit offers a potent resource for those interested in not only phenomenology but in understanding its legacy for several important continental philosophers. The specific achievement of Sebbah's text lies with his careful and revealing readings of Jacques Derrida, Emmanuel Levinas, Michel Henry, and Jean-Luc Marion among a group of French philosophers whose concern with issues of givenness, time, and subjectivity all ultimately derive from Husserl and thus share a certain family resemblance. Sebbah refers to these philosophers as phenomenologists, a key decision no doubt meant to provoke. It is no surprise to discuss Henry or Marion as phenomenologists, but eyebrows might be raised with respect to Derrida and Levinas. Even though Levinas studied with both Husserl and Heidegger in Freiburg, and Derrida spent the first decade of his career writing about Husserl, both thinkers are generally thought of as breaking with phenomenology in order to establish their own philosophical projects. Carefully avoiding the politics of labeling philosophers as phenomenologists with a capital P, Sebbah admirably explores their work as operating within the longer tradition of phenomenology in a refreshingly nonpartisan and nongenerationalist manner. Sebbah synthesizes the work of these often very different philosophers by devoting himself to specifying how each operates at the extreme limits of Husserlian phenomenology. To pursue this project, Sebbah explicitly suspends consideration of phenomenology's more recent theological turn (and [End Page 156] silently suspends analysis of similarly contemporary efforts to naturalize phenomenology in concert with research in cognitive science and neuroscience).2 At least in the North American context, the impact of this decision registers beyond the already significant but ultimately highly specialized payoff associated with the astute reconsideration of...

In contemporary political science, many believe that normative restrictions on armed conflict are an outgrowth of Western culture and the just war tradition. Drawing on historical evidence, which shows that political actors in Ancient China and the early Islamic empire endorsed civilian protection rules, I claim that such norms are more common than most International Relations (IR) theorists suppose. For IR theory, this raises an important puzzle: How can we explain why similar normative ideas emerged in human societies that are otherwise very different? Building on research in cognitive science, social psychology, and social neuroscience, I argue that most people have natural cognitive and emotional predispositions that bias the emergence and transmission of cultural norms that protect non-combatants. More specifically, capacities for perspective-taking and empathy shape how people interpret the limits of their moral commitments, and when these capacities are engaged, intuitional heuristics affect how they judge the morality of killing in war. What is more, I claim that three key contextual variables moderate the connection between innate moral intuitions and the development of civilian protection norms: (i) societal interdependence, (ii) the social empowerment of marginal actors, and (iii) the creation of norms in argumentative contexts that require impartial moral reasoning. I argue that rationalist and constructivist theories of norm emergence will be able to better explain the emergence, the durability, and the institutional design of the norms of war by incorporating this naturalistic theory of moral cognition.

Meta-learning, social cognition and consciousness in brains and machines

In this final set of explorations/meditations (of three), we examine the requirements for developing artificial general intelligence (AGI) through the lens of human cognitive architecture, with particular emphasis on the role of narrative selfhood and social cognition. Drawing on perspectives from cognitive science, philosophy of mind, and artificial intelligence research, we critically evaluate current claims about the capabilities of large language models, particularly regarding their purported achievements of theory of mind and self-awareness. We argue that genuinely human-like artificial intelligence may require more than sophisticated pattern recognition and language modeling, potentially necessitating the development of coherent narrative self-models and rich causal understanding. Special attention is given to the relationship between consciousness, conscience, and trustworthy AI systems, suggesting that meaningful artificial intelligence may require forms of richly-embodied and socially-embedded development to achieve robust and reliable functionality. We conclude by proposing that the path to artificial general intelligence may require recapitulating aspects of human cognitive development, particularly regarding the construction of narrative identity and social-moral reasoning capabilities. This analysis has implications for both the technical development of AI systems and the ethical frameworks through which we evaluate artificial minds.