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When an application needs a fusion mechanism, the developer has to solve an essential problem: the construction of the appropriate model. This corresponds to (i) the selection of an aggregation operator and (ii) the determination of its parameters. This process should take into account several factors. Some of them are highlighted here. [ABSTRACT FROM AUTHOR]

This chapter reviews some of the existing tools for evaluating aggregation methods and their parameters. We focus on some indices for fuzzy measures (Shapley and Banzhaf), an interaction index, and the degree of disjunction. Other methods exist. The influence function and other tools such as grosserror sensitivity and local-shift sensitivity developed in robust statistics (see Section 2.2.6) are of interest here. The tools permit us to have some knowledge on how a particular estimator might behave when embedded in a real system. In particular, we have seen that the influence function of the arithmetic mean is unbounded while that of the median is bounded. [ABSTRACT FROM AUTHOR]

In this chapter we review some aggregation operators for numerical information. While in Chapter 4 description was centered on functional equations, and operators were introduced as a natural consequence of some basic properties (unanimity, positive homogeneity, and so on), here, operators are introduced for greater modeling capabilities and generality. This progression into general aggregation operators leads to a review of operators that are particular cases of Choquet and Sugeno integrals. On the one hand, the Choquet integral generalizes not only arithmetic mean and weighted mean (the most widely used and well-known aggregation operators), but also OWA operators. On the other hand, the Sugeno integral generalizes weighted minimum, weighted maximum, and median operators. In the rest of this chapter we will use Choquet integral family to refer to aggregation operators that are generalized by the Choquet integral. In the same way, the Sugeno integral family will refer to aggregation operators that the Sugeno integral generalizes. [ABSTRACT FROM AUTHOR]

Most aggregation operators use some kind of parameterization to express additional information about the objects that take part in the aggregation process. Applying the jargon of artificial intelligence, we can say that the parameters are used to represent the background knowledge. For example, it is well known that in the case of the weighted mean, the weights — i.e., the weighting vector — play this role. In an application, we can use them to express the reliability of the information sources (sensors, experts, and so on). For example, when fusing data from sensors, we can express wich sensor is more likely to give data of better quality and which is more likely to give erroneous data. In a similar way, other aggregation functions use other parameterizations. [ABSTRACT FROM AUTHOR]

In this chapter we study some aggregation operators for numerical information. The description is focused on results based on functional equations. Therefore, not only are the operators given, but also, at least for some of them, their characterization. We refer to these results as syntheses of judgements. Although the term could be used for any aggregation operator, we restrict its use to the case of characterizations using functional equations. [ABSTRACT FROM AUTHOR]

Functional equations are equations where the unknowns are functions. A well-known example of functional equation is the following Cauchy equation: (3.1)$$ \phi (x + y) = \phi (x) + \phi (y). $$ A function φ is a solution of this equation if, for any two values x and y, the application of φ to x + y equals the addition of the application of φ to x and to y. Therefore, the equation establishes conditions that functions φ have to satisfy. Typical solutions of this Cauchy equation are the functions φ(x) = αx for an arbitrary value for α. [ABSTRACT FROM AUTHOR]

In this chapter we will review some of the concepts that are needed later in the book. In particular, we focus on measurement theory and some basic elements of probability theory and fuzzy sets theory. [ABSTRACT FROM AUTHOR]

Information fusion techniques, in general, and aggregation operators (or aggregation functions), in particular, are extensively used in several fields of human knowledge. They are used to produce the most comprehensive and specific datum about an entity from data supplied by several information sources (or the same source at different periods of time). They are used in systems to reduce some type of noise, increase accuracy, summarize information, extract information, make decisions, and so on. To illustrate this, we consider below some examples in different fields. Some of the typical applications are also included. [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]