Search

The Turing Test (TT) is criticised for various reasons, one being that it is limited to testing only human-like intelligence. We can read, for example, that ‘TT is testing humanity, not intelligence,’ (Fostel, 1993), that TT is ‘a test for human intelligence, not intelligence in general,’ (French, 1990), or that a perspective assumed by TT is parochial, arrogant and, generally, ‘massively anthropocentric’ (Hayes and Ford, 1996). This limitation presumably causes a basic inadequacy of TT, namely that it misses a wide range of intelligence by focusing on one possibility only, namely on human intelligence. The spirit of TT enforces making explanations of possible machine intelligence in terms of what is known about intelligence in humans, thus possible specificity of the computer intelligence is ruled out from the oaelset. This approach causes ire in some interpreters of the test and leads them to desire to create a theory of intelligence in general, thereby overcoming the limitations imposed by merely human intelligence. At times it is an emotion-laden discussion that does not hesitate to impute chauvinism in those limiting themselves to human-type intelligence.1 This discussion is, by the way, not unlike the rhetoric used by some defenders of animal rights, who insist that an expression of superiority of men over animals is a token of speciesism, and ‘speciesism is just a moral mistake of the same sort as racism and sexism’.

My claim is clear and unambiguous: no machine will pass a well-designed Turing Test unless we find some means of embedding it in lived social life. We have no idea how to do this but my argument, and all our evidence, suggests that it will not be a necessary condition that the machine have more than a minimal body. Exactly how minimal is still being worked out.

This paper characterizes the methodology of Artificial Intelligence by looking at research in speech understanding, a field where AI approaches contrast starkly with the alternatives, particularly engineering approaches. Four values of AI stand out as influential: ambitious goals, introspective plausibility, computational elegance, and wide significance. The paper also discusses the utility and larger significance of these values.

John Haugeland's Mind design and Mind design II are organized around the idea that the fundamental idea of cognitive science is that, “intelligent beings are semantic engines—in other words, automatic formal systems with interpretations under which they consistently make sense”. The goal of artificial intelligence research, or the problem of “mind design” as Haugeland calls it, is to develop computers that are in fact semantic engines. This paper canvasses the changes in artificial intelligence research reflected in the different selections of essays found in each volume. While Mind design II is a worthy successor to Mind design, there are some notable developments in artificial intelligence which suggest that seemingly intelligent behavior need not be guided by semantic engines at all.

To explore our field's future, we invited a number of well-known AI scientists to contribute articles speculating about where AI is headed and how we might get there. This article is part of a special issue on the Future of AI.

Exactly whose childhood do I remember? Why is it that splashes leave only ripples? Could I copy myself into a computer? These three questions have, over the years, shaped my perception of the universe, of science, and above all of artificial intelligence. The first is a question about materialism, the second about persistence, and the third about simulation. My attempts to answer them have brought me firmly into the strong camps of both AI and artificial life.

A short overview of artificial intelligence and its relationship with fuzzy logic is provided. We emphasize the role fuzzy logics can play in extending some of the models of Artificial Intelligence.

This paper discusses the subject of applying artificial Intelligence (AI) techniques to automatic test. It has been the experience of the author that AI is not a "magic bullet", and that its successful use requires an understanding of both the technology and the repair process to which it is being applied.

The nature and history of the research area common to artificial intelligence and symbolic mathematical computation are examined, with particular reference to the topics having the greatest current amount of activity or potential for further development: mathematical knowledgedbased computing environments, autonomous agents and multidagent systems, transformation of problem descriptions in logics into algebraic forms, exploitation of machine learning, qualitative reasoning, and constraintdbased programming. Knowledge representation, for mathematical knowledge, is identified as a central focus for much of this work. Several promising topics for further research are stated.

A survey of the main approaches in a mind study -oriented part of Artificial Intelligence is made focusing on controversial issues and extreme hypotheses. Various meanings of terms: "intelligence" and "artificial intelligence" are discussed. Limitations for constructing intelligent systems resulting from the lack of formalized models of cognitive activity are shown. The approaches surveyed are then recapitulated in the light of these limitations.

This paper is about problems in the area of the artificial intelligence (AI) of an embodied agent, i.e. a robot or any other autonomous machine, whether simulated or real. We discuss the necessity of grounding the concepts that form relations between symbols in the decision unit of such an agent. We explore existing concepts for knowledge engineering in the field of the Semantic Technologies to determine which grounded base vocabulary might be necessary, we detect that besides bodily experienced basics intelligence also deals with mental experiences and show a possible way to ground the concept for subclass as an example.

Complexity of technology is constantly increasing; for the field of automation this means that economic considerations dictate a need for corresponding measures. Artificial intelligence boasts noteworthy successes in this area; however, its achievements appear modest when compared to the faculties of human intelligence. This paper will demonstrate that a new modeling approach is required via possibilities offered by the mindset and tools of computer technology, thereby demonstrating why a psychoanalytic approach seems sensible and necessary. The paramount goal of the research introduced in the following is a formal approach to describing the human psyche based on the neuro- symbolic and neuronal functional layers. Questions associated with this approach include: To what extent can the psychoanalytic model be confirmed by computer technology and the possibility of simulation? To what extent is an axiomatically developed terminology in psychoanalysis a prerequisite for the better integration of the natural-scientific way of thinking in psychoanalysis? The paper is based on the results of several fundamental research projects within the framework of ARS (Artificial Recognition System) which were funded in part nationally and in part by the EU.

This chapter presents a brief overview of advances in modern artificial intelligence. It recognises that society has embraced Artificial Intelligence (AI), even if it is embedded within many of the consumer products being marketed. The reality is that society is already in the throws of digitizing its past and continues progressively moves on-line. The volume and breadth of data being processed is becoming unfathomable. This digital future heralds the dawn of virtual communities, operating a Web of Things (WoT) full of connected devices, many fitted with wireless connectivity 24/7. This pervasiveness increases the demand on researchers to provide more intelligent tools, capable of assisting humans in prosecuting this information, seamlessly, efficiently and immediately. Ultimately AI techniques have been evolving since the 1950s. This evolution began with Good Old-Fashioned Artificial Intelligence (GOFAI) using explicitly coded knowledge, heuristics and axiomatization. This digital analogy of biological systems initially failed to realise its potential, at least until the birth of personal computers. This introduced a paradigm shift towards the Fuzzy/Neural era, which furnished society with computer vision, character recognition and Evolutionary Computing (EC) (among other successes). The value engineering proponents continued to invest in automation, which spurred the growth of Machine Intelligence (MI) research, further increasing expectations for computers to do more with less human interaction. McCarthy recently agreed that it is now more appropriate to reliable AI research as Computational Intelligence (CI), because primitive methodologies have matured and science continues to witness more hybrid solutions. It is true that modern AI techniques typically employ multiple techniques and many now form hybrid systems with flexible problem solving capabilities or increased autonomy. This book contains a series of topics aimed at illustrating advances in modern AI. This book provides discussion on a number of recent innovations that include: classifiers, neural networks, fuzzy logic, Multi-Agent Systems (MASs) and several example applications.

During the past 70+ years of research and development in the domain of Artificial Intelligence (AI) we observe three principal, historical waves: embryonic, embedded and embodied AI. As the first two waves have demonstrated huge potential to seed new technologies and provide tangible business results, we describe likely developments of embodied AI in the next 25-35 years. We postulate that the famous Turing Test was a noble goal for AI scientists, making key, historical inroads - while we believe that Biological Systems Intelligence and the Insect/Swarm Intelligence analogy/mimicry, though largely disregarded, represents the key to further developments. We describe briefly the key lines of past and ongoing research, and outline likely future developments in this remarkable field.

Despite some significant achievements in the early stage of works on the development of artificial intelligence, scientists failed to program machines to imitate human thinking. The next generation of scientists included proposals of the embodied mind paradigm in their new research programme. The paradigm states that human intelligence is formed through a reciprocal interaction between the body and an environment. This work discusses the application of the main proposals of the new artificial intelligence that were applied in the process of constructing machines and modelling their behaviour. It presents important projects that met the philosophical criteria and that were aimed at embodying artificial intelligence.

As modern games become more and more sophisticatedgraphically, so does the level of artificial intelligence thatanimates them thus, the larger the game budget, the more workis put into improving the AI. Unfortunately many of these gamesfeature AIs that are standalone and do not communicate witheach other, they do not try to negotiate in order to improvetheir individual standing. The current work focuses on analysingexisting game types in order to establish types of negotiation thatcan be achieved between AI entities. Moreover, an evolutionaryapproach which focuses on achieving negotiation between theseentities and tackles the problem of having multiple negotiationitems with discrete values is presented.

The preceding chapters focused upon introducing the characteristics within an Information Continuum and how they relate to a fully autonomous, learning, reasoning system (analogous to a synthetic brain), and how a SELF must possess constructs in its hardware and software to mimic humanistic processes and subsystems . This chapter will focus more upon designing and implementing these humanistic structures by understanding how they must interact and cooperate in order to form a comprehensive learning system. We employ concepts adapted from the domain of cognitive psychology as inputs into the formation of these interactive humanistic structures, sub-structures, and components. In short, Psychology helps us understand how these structures function within the human brain followed by translation efforts to design and implement these dynamic functions within an analogous synthetic brain. Hence, the foundational building blocks likened to “synthetic consciousness”, comprised of cognition, intuition, and other capabilities that humans possess.

In the centenary year of Turing's birth it is appropriate to explore the relationship between Computational and Human Intelligence along the path that he proposed over 60 years ago. In many way, he saw clearly what the issues were, although there were some that he missed or underestimated. We approach the problem of Human-level Computational Intelligence from two perspectives: the aspects of human cognition that we want to achieve, and those that we need to achieve for the system to work and achieve our primary goals. The first set of characteristics are useful in their own right, whilst the second vista has an even more fundamental utility as heuristics that allow us to wend a path through a mire of computability and complexity issues. This paper explores a 35 year program of research into theoretical understanding and computational modelling, with implementation of human-like language and learning capabilities based on psycholinguistic principles from the study of human language learning.

CogPrime, a comprehensive architecture for embodied Artificial General Intelligence, is reviewed, covering the core architecture and algorithms, the underlying conceptual motivations, and the emergent structures, dynamics and functionalities expected to arise in a completely implemented CogPrime system once it has undergone appropriate experience and education. A qualitative argument is sketched, in favor of the assertion that a completed CogPrime system, given a modest amount of experience in an embodiment enabling it to experience a reasonably rich human-like world, will give rise to human-level general intelligence (with significant difference from humans, and with potential for progress beyond this level).

Natural language processing.- Evolutionary and nature-inspired metaheuristic algorithms.- Neural networks and hybrid intelligent systems.- Fuzzy systems and probabilistic models in decision making.

Since John McCarthy at MIT coined the term artificial intelligence in 1956 aiming to make a machine have a human-like intelligence in a visible future, we have had lots of discussions whether it is possible in a true sense, and lots of intelligent machines have been reported. Nowadays, the term is ubiquitous in our community. In this chapter we discuss how those proposed machine intelligences are actually intelligent. Starting with how we define intelligence, how can we measure it, how those measurements really represent intelligence and so on, by surveying the Legg and Hutter’s seminal paper on formal definition of machine intelligence, we name a few others, taking a brief look at our own too. We also consider a modern interpretation of the Turing test originally proposed in 1950. Then we argue a benchmark to test how an application is intelligent by means of an algorithm for stock market investment as an example. Finally we take a consideration of how we can achieve a human intelligence in a real sense in a real visible future, including an analysis of IT changes stimulating artificial intelligence development.

Artificial intelligence (AI) is a relatively young discipline, yet it has already led to general-purpose problem-solving methods and novel applications. Ultimately, AI's goals of creating models and mechanisms of intelligent action can be realized only in the broader context of computer science. Creating mechanisms for sharing of knowledge, knowhow, and literacy is the challenge. The great Chinese philosopher Kuan-Tzu once said: "If you give a fish to a man, you will feed him for a day. If you give him a fishing rod, you will feed him for life." We can go one step further: If we can provide him with the knowledge and the know-how for making that fishing rod, we can feed the whole village. Therein lies the promise-and the challenge-of AI.

In contrast to intelligence, wisdom is the part of the propositional knowledge part of the background of knowledge that is a mean between two extremes of believing without sufficient evidence and not believing with sufficient evidence. Wisdom may be incorporated in an artificial intelligence system as artificial wisdom (AW). An intelligence system with AW, will learn a collection of activities like a human. AW may be defined as the process of indwelling existing wisdom that is formed from conceiving knowledge. Knowledge is formed on crashing structured information. Indwelling is the process of generating links among different knowledge modules. Links will have strengths that will define the efficiency of the AW. Strengths will depend on the metafunction equivalents of the background of knowledge that are analyzing, synthesizing and imagining, and valuing. These metafunction equivalents are to be set against key temperament characteristics of AW that are `allergy to ambiguity', `conformity', `rigidity', `starved sensibilities' etc.

There is a certain tendency to consider that, whereas approaches based on artificial life may be appropriate for lower-level cognition, the computational theory of mind based primarily on the manipulation of symbolic representations is the only possible approach to high-level cognition. This article contests that view, and argues that a constructivist approach to cognition rooted in elementary living organisms leads to a radically new understanding of phenomena such as communication, representation, intentional action and language. Whatever the level of cognition, the computational paradigm is necessarily objectivist, whereas the constructivist paradigm is non-objectivist. It is suggested that within the field of Artificial Life, objectivism is appropriate from an engineering perspective, but that constructivism is appropriate from a biological perspective aimed at modelling living organisms as autonomous systems.

Artificial Intelligence (AI) is the branch of computer science which seeks to model intelligent human behavior on a computer. In this paper the way in which symbolic AI is predicated on a traditional rationalist epistemology is described. Traditional criticisms of AI converge on the possibility of creating true artificial intelligence, whereas a feminist argument looks instead to the cultural setting of AI — whose knowledge and what type of knowledge is to be represented. Feminist epistemology can be used to support and to extend these arguments in two main directions, both of which have links to other philosophical or sociological traditions. The first direction focuses on the knowing subject and the second is concerned with the distinction between “knowing that” and “knowing how,” or the prepositional/skills distinction.

So what does an artificial intelligence do with itself after it has become self-aware? Suppose that we do succeed in creating an AI. Or suppose that an AI emerges spontaneously out of data networks' growing complexity. What then-from the point of view of the AI? Science fiction provides some interesting thought experiments on the subject of AI motivation.

Almost all of the progress of artificial intelligence in the last 50 years has been based on the Turing model and Von Neumann architecture. Researchers have always tried to put the human intelligence into machines by the ways of algorithms, codes or symbols that could be understood and executed by machines, thus, we may be bounded to Turing model too tightly. In Internet and World Wide Web and developing cloud computing, network has changed the role from a single huge Turing machine or sum of some Turing machines to the collective intelligence, where the inputs or outputs of nodes in network are happening not only among computers, but also among people, such that Internet has been beyond Turing machine. Users in Internet who own similar interests may cluster naturally into scalable and boundless communities with uncertainty, where online interaction avoids the difficulty of common sense representation in traditional artificial intelligence. Furthermore, collective intelligence may emerge from the crowds interaction. Those would become the new research frontiers in intelligence science.

Machine ethics and robot rights are quickly becoming hot topics in artificial intelligence and robotics communities. We will argue that attempts to attribute moral agency and assign rights to all intelligent machines are misguided, whether applied to infrahuman or superhuman AIs, as are proposals to limit the negative effects of AIs by constraining their behavior. As an alternative, we propose a new science of safety engineering for intelligent artificial agents based on maximizing for what humans value. In particular, we challenge the scientific community to develop intelligent systems that have human-friendly values that they provably retain, even under recursive self-improvement.

Throughout the history of the artificial intelligence movement, researchers have strived to create computers that could simulate general human intelligence. This paper argues that workers in artificial intelligence have failed to achieve this goal because they adopted the wrong model of human behavior and intelligence, namely a cognitive essentialist model with origins in the traditional philosophies of natural intelligence. An analysis of the word “intelligence” suggests that it originally referred to behavior-environment relations and not to inferred internal structures and processes. It is concluded that if workers in artificial intelligence are to succeed in their general goal, then they must design machines that are adaptive, that is, that can learn. Thus, artificial intelligence researchers must discard their essentialist model of natural intelligence and adopt a selectionist model instead. Such a strategic change should lead them to the science of behavior analysis.

In this paper, we study the Natural Language Processing in the communication management system and utilize the principle of Finite Automata Machine (FAM) in the AI theory to propose a simple and effective algorithm for Natural Language Processing. Furthermore, we present the structure model of the new algorithm and parameters configuration. The experimental results of our testing show that if we use this new algorithm neatly, it will bring us very good performance.

In this chapter, I want to examine two famous discussions of philosophical consequences of the connectionist approach. Naturally, these two discussions are but a small selection of the responses and criticisms that in fact have been made to the development of connectionism. But I have selected these two discussions in particular because they touch on themes that have already been — and will continue to be — important to the particular account being presented in this book. They concern connectionism’s implications for the mind-body problem, and the relationship between connectionism and GOFAI (with the latter also setting the stage for a subsequent discussion of connectionism’s relationship to the dynamical systems approach to cognitive science and AI).

International audience; This volume contains the papers selected for presentation at JELIA 2012. Competition was very high this year. We received 107 submissions from 31 countries (97 regular papers and 10 system descriptions). Only 36 regular papers and 5 system descriptions were selected for inclusion in the proceedings. The program included three invited talks whose abstracts can be found below: – Leila Amgoud and Philippe Besnard “Logical Limits of Dung’s Abstract Argumentation Framework” – Ulrich Furbach “Extensions of Hyper Tableaux” – Wiebe van der Hoek “On Two Results in Contemporary Modal Logic: Local Definability and Succinctness”

By exploring the relationships between different AGI architectures, one can work toward a holistic cognitive model of human-level intelligence. In this vein, here an integrative architecture diagram for human-like general intelligence is proposed, via merging of lightly modified version of prior diagrams including Aaron Sloman’s high-level cognitive model, Stan Franklin and the LIDA group’s model of working memory and the cognitive cycle, Joscha Bach and Dietrich Dorner’s Psi model of motivated action and cognition, James Albus’s three-hierarchy intelligent robotics model, and the author’s prior work on cognitive synergy in deliberative thought and metacognition, along with ideas from deep learning and computational linguistics.

The paper examines information-computation processes in time and in space and some aspects of computer intelligence using multidimensional matrix neural growing networks. In particular, issues of object-oriented “thinking” of computers are considered.

My goal is to emphasize the way we generally use the word ‘logic’ and the sort of problems related to the definition of logic and the sort of problems related to the definition of logic. I also wish to underline the differences between human intelligence and artificial intelligence.

The term ‘the artificial’ can only be given a precise meaning in the context of the evolution of computational technology and this in turn can only be fully understood within a cultural setting that includes an epistemological perspective. The argument is illustrated in two case studies from the history of computational machinery: the first calculating machines and the first programmable computers. In the early years of electronic computers, the dominant form of computing was data processing which was a reflection of the dominant philosophy of logical positivism. By contrast, artificial intelligence (AI) adopted an anti-positivist position which left it marginalised until the 1980s when two camps emerged: technical AI which reverted to positivism, and strong AI which reified intelligence. Strong AI's commitment to the computer as a symbol processing machine and its use of models links it to late-modernism. The more directly experiential Virtual Reality (VR) more closely reflects the contemporary cultural climate of postmodernism. It is VR, rather than AI, that is more likely to form the basis of a culture of the artificial.