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This book presents biologically inspired walking machines (four- and six-legged walking machines) interacting with their real environmental stimuli as agent-environment interactions. Different reactive behaviors of animals were investigated for the behavior design of the walking machine(s). On the one hand, the obstacle avoidance behavior, in analogy to the obstacle avoidance and escape behavior of scorpions and cockroaches, was implemented in the walking machines as a negative tropism. On the other hand, the sound tropism which mimics prey capture behavior of spiders is represented as a positive tropism. It was simulated on the four-legged walking machine. [ABSTRACT FROM AUTHOR]

Where Chap. 2 investigated the biologically inspired perception-action systems, this chapter focuses on applying the principles of the biological domain to create artificial perception-action systems. First, several preprocessing units of different types of sensory signals are presented. They are used to filter and recognize the corresponding sensory signals and they can be described as perception parts. Second, the neural control of the four- and six-legged walking machines, which generates and controls the locomotion of the machines, is described. Third, the combination of the neural preprocessing and the neural control is explained. It gives rise to the ability of controlling reactive behaviors such as obstacle avoidance and sound tropism. Finally, both behavior controls are merged under a so-called behavior fusion controller by applying a sensor fusion technique to give a versatile perception-action system. [ABSTRACT FROM AUTHOR]

In order to test the capabilities of the artificial perception-action systems, several experiments were carried out. First, the signal processing networks were tested with the simulated signals and the real sensor signals. Afterwards the physical sensors, the neural preprocessing and the neural control were all together implemented on the physical walking machine(s) to demonstrate different reactive behaviors. [ABSTRACT FROM AUTHOR]

This chapter describes the development of the physical components that lead to the artificial perception-action systems. It begins with the descriptions of different physical sensors which are used to sense environmental information, followed by the details of the walking machines simulated in a physical simulation environment as well as the robots we have built. [ABSTRACT FROM AUTHOR]

This chapter presents methods and tools which are to be used throughout this book. It starts with a short introduction to a biological neuron together with an artificial neuron which is followed by the comparison of network structures between feedforward and recurrent neural networks. Then the discrete-time dynamical properties of the single neuron with a recurrent connection are described. Finally, artificial evolution is presented as a tool to develop and optimize neural structures as well as the strength of synapses. [ABSTRACT FROM AUTHOR]

Most of this book is devoted to creating and demonstrating so-called artificial perception-action systems inspired by biological sensing systems (perception) and animal behavior (action). Thus this chapter attempts to provide the biological background for understanding the approach taken in this book. It begins with a short introduction to some of the necessary principles of animal behavior. Then it concentrates on the obstacle avoidance and escape behavior of a scorpion and a cockroach, and continues with the prey capture behavior of a spider. Here, attention is given to the biological sensing systems used to trigger the described behaviors. Furthermore, different morphologies of walking animals are presented as inspiration for the design of walking machine platforms. Finally, a biologically inspired locomotion control, called a "central pattern generator" (CPG), is also discussed. This concept is later employed to generate the rhythmic leg movements of the machines. [ABSTRACT FROM AUTHOR]

Research in the domain of biologically inspired walking machines has been ongoing for over 20 years [59, 166, 190, 199, 207]. Most of it has focused on the construction of such machines [34, 47, 216, 223], on a dynamic gait control [43, 117, 201] and on the generation of an advanced locomotion control [30, 56, 104, 120], for instance on rough terrain [5, 66, 102, 180, 192]. In general, these walking machines were solely designed for the purpose of motion without responding to environmental stimuli. However, from this research area, only a few works have presented physical walking machines reacting to an environmental stimulus using different approaches [6, 36, 72, 95]. On the one hand, this shows that less attention has been paid to walking machines performing reactive behaviors. On the other hand, such complex systems can serve as a methodology for the study of embodied systems consisting of sensors and actuators for explicit agent-environment interactions. [ABSTRACT FROM AUTHOR]

Section 1 discusses the conceptual foundations of general intelligence as a discipline, orienting it within the Integrated Causal Model of Tooby and Cosmides; Section 2 constitutes the bulk of the paper and discusses the functional decomposition of general intelligence into a complex supersystem of interdependent internally specialized processes, and structures the description using five successive levels of functional organization: Code, sensory modalities, concepts, thoughts, and deliberation. Section 3 discusses probable differences between humans and AIs and points out several fundamental advantages that minds-in-general potentially possess relative to current evolved intelligences, especially with respect to recursive self-improvement. [ABSTRACT FROM AUTHOR]

The proposal is a radical one — that human cognition is significantly weaker than we presume and AI significantly closer than we dared hope. I believe that the human mind is largely made up of tricks and sleights of hand that enamor us with much pride; but our pedestal might not be quite so high or robust as we imagine. I will pursue the argument that human cognition is based largely on 3D simulation and as such is particularly vulnerable to co-option by future advances in animation software. [ABSTRACT FROM AUTHOR]

The chapter argues that the investigations of evolutionary processes that result in human intelligence by means of mathematical/computer models can be a serious scientific basis of AI research. The "intelligent inventions" of biological evolution (unconditional reflex, habituation, conditional reflex...) to be modeled, conceptual background theories (the metasystem transition theory by V.F. Turchin and the theory of functional systems by P.K. Anokhin) and modern approaches (Artificial Life, Simulation of Adaptive Behavior) to such modeling are outlined. Two concrete computer models, "Model of Evolutionary Emergence of Purposeful Adaptive Behavior" and the "Model of Evolution of Web Agents" are described. The first model is a pure scientific investigation; the second model is a step to practical applications. Finally, a possible way from these simple models to implementation of high level intelligence is outlined. [ABSTRACT FROM AUTHOR]

It is difficult to develop an adequate mathematical definition of intelligence. Therefore we consider the general problem of searching for programs with specified properties and we argue, using the Church-Turing thesis, that it covers the informal meaning of intelligence. The program search algorithm can also be used to optimise its own structure and learn in this way. Thus, developing a practical program search algorithm is a way to create AI. To construct a working program search algorithm we show a model of programs and logic in which specifications and proofs of program properties can be understood in a natural way. We combine it with an extensive parser and show how efficient machine code can be generated for programs in this model. In this way we construct a system which communicates in precise natural language and where programming and reasoning can be effectively automated. [ABSTRACT FROM AUTHOR]

Sequential decision theory formally solves the problem of rational agents in uncertain worlds if the true environmental prior probability distribution is known. Solomonoff's theory of universal induction formally solves the problem of sequence prediction for unknown prior distribution. We combine both ideas and get a parameter-free theory of universal Artificial Intelligence. We give strong arguments that the resulting AIXI model is the most intelligent unbiased agent possible. We outline how the AIXI model can formally solve a number of problem classes, including sequence prediction, strategic games, function minimization, reinforcement and supervised learning. The major drawback of the AIXI model is that it is un-computable. To overcome this problem, we construct a modified algorithm AIXItl that is still effectively more intelligent than any other time t and length l bounded agent. The computation time of AIXItl is of the order t·2l. The discussion includes formal definitions of intelligence order relations, the horizon problem and relations of the AIXI theory to other AI approaches. [ABSTRACT FROM AUTHOR]

We present the first class of mathematically rigorous, general, fully self-referential, self-improving, optimally efficient problem solvers. Inspired by Kurt Gödel's celebrated self-referential formulas (1931), such a problem solver rewrites any part of its own code as soon as it has found a proof that the rewrite is useful, where the problem-dependent utility function and the hardware and the entire initial code are described by axioms encoded in an initial proof searcher which is also part of the initial code. The searcher systematically and efficiently tests computable proof techniques (programs whose outputs are proofs) until it finds a provably useful, computable self-rewrite. We show that such a self-rewrite is globally optimal—no local maxima!—since the code first had to prove that it is not useful to continue the proof search for alternative self-rewrites. Unlike previous non-self-referential methods based on hardwired proof searchers, ours not only boasts an optimal order of complexity but can optimally reduce any slowdowns hidden by the O()-notation, provided the utility of such speed-ups is provable at all. [ABSTRACT FROM AUTHOR]

Most traditional artificial intelligence (AI) systems of the past 50 years are either very limited, or based on heuristics, or both. The new millennium, however, has brought substantial progress in the field of theoretically optimal and practically feasible algorithms for prediction, search, inductive inference based on Occam's razor, problem solving, decision making, and reinforcement learning in environments of a very general type. Since inductive inference is at the heart of all inductive sciences, some of the results are relevant not only for AI and computer science but also for physics, provoking nontraditional predictions based on Zuse's thesis of the computer-generated universe. [ABSTRACT FROM AUTHOR]

This chapter introduces the idea of "Evolvable Hardware," which applies evolutionary algorithms to the generation of programmable hardware as a means of achieving Artificial Intelligence. Cellular Automata-based Neural Networks are evolved in different modules, which form the components of artificial brains. Results from past models and plans for future work are presented. [ABSTRACT FROM AUTHOR]

Understanding general intelligence and identifying its essential components are key to building next-generation AI systems — systems that are far less expensive, yet significantly more capable. In addition to concentrating on general learning abilities, a fast-track approach should also seek a path of least resistance — one that capitalizes on human engineering strengths and available technology. Sometimes, this involves selecting the AI road less traveled. I believe that the theoretical model, cognitive components, and framework described above, joined with my other strategic design decisions provide a solid basis for achieving practical AGI capabilities in the foreseeable future. Successful implementation will significantly address many traditional problems of AI. Potential benefits include: minimizing initial environment-specific programming (through self-adaptive configuration);substantially reducing ongoing software changes, because a large amount of additional functionality and knowledge will be acquired autonomously via self-supervised learning;greatly increasing the scope of applications, as users teach and train additional capabilities; andimproved flexibility and robustness resulting from systems' ability to adapt to changing data patterns, environments and goals. AGI promises to make an important contribution toward realizing software and robotic systems that are more usable, intelligent, and human-friendly. The time seems ripe for a major initiative down this new path of human advancement that is now open to us. [ABSTRACT FROM AUTHOR]

The Novamente AI Engine, a novel AI software system, is briefly reviewed. Novamente is an integrative artificial general intelligence design, which integrates aspects of many prior AI projects and paradigms, including symbolic, probabilistic, evolutionary programming and reinforcement learning approaches; but its overall architecture is unique, drawing on system-theoretic ideas regarding complex mental dynamics and associated emergent patterns. The chapter reviews both the conceptual models of mind and intelligence which inspired the system design, and the concrete architecture of Novamente as a software system. [ABSTRACT FROM AUTHOR]

Is there an "essence of intelligence" that distinguishes intelligent systems from non-intelligent systems? If there is, then what is it? This chapter suggests an answer to these questions by introducing the ideas behind the NARS (Nonaxiomatic Reasoning System) project. NARS is based on the opinion that the essence of intelligence is the ability to adapt with insufficient knowledge and resources. According to this belief, the author has designed a novel formal logic, and implemented it in a computer system. Such a "logic of intelligence" provides a unified explanation for many cognitive functions of the human mind, and is also concrete enough to guide the actual building of a general purpose "thinking machine". [ABSTRACT FROM AUTHOR]