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Modern deep learning systems for medical image classification have demonstrated exceptional capabilities for distinguishing between image based medical categories. However, they are severely hindered by their ina-bility to explain the reasoning behind their decision making. This is partly due to the uninterpretable continuous latent representations of neural net-works. Emergent languages (EL) have recently been shown to enhance the capabilities of neural networks by equipping them with symbolic represen-tations in the framework of referential games. Symbolic representations are one of the cornerstones of highly explainable good old fashioned AI (GOFAI) systems. In this work, we demonstrate for the first time, the emer-gence of deep symbolic representations of emergent language in the frame-work of image classification. We show that EL based classification models can perform as well as, if not better than state of the art deep learning mod-els. In addition, they provide a symbolic representation that opens up an entire field of possibilities of interpretable GOFAI methods involving symbol manipulation. We demonstrate the EL classification framework on immune cell marker based cell classification and chest X-ray classification using the CheXpert dataset. Code is available online at https://github.com/AriChow/EL.

Lean Manufacturing is well known as an effective means to improve productivity and decrease costs of operations, using a series of management practices developed first in Japan and then adapted to worldwide circumstances. Rapid technological progress has opened new business potentials and opportunities forcing companies to constantly introduce more and more advanced solutions to remain competitive. However, the potential of such technologies to support the implementation of Lean Manufacturing is not completely perceived yet. After defining Lean 4.0 which aims to provide useful insight for the integration of lean and Industry 4.0 in the manufacturing companies, AI is then briefly introduced, as a key enabler. This paper focuses on the integration and support of AI to Lean 4.0, mainly investigating the process and control issues through better use of data and knowledge.

In this paper, we discuss the requirements for an agent- and role-based planning approach for flexible automation at advanced production systems. Special attention is given to balanced automation systems involving humans cooperating and/or collaborating with robots, and to processes interoperability between human agents and artificial (robot) agents towards flexible production system. This application context combines the research needs from fields of the Operator 4.0, Collaborative Robotics, Symbolic Artificial Intelligence and Automatic Control. We provide a brief overview of the contextualized state-of-the-art and discuss approaches towards flexible automation in advanced production systems.

Arguably the grand goal of artificial intelligence research is to produce machines with general intelligence: the capacity to solve multiple problems, not just one. Artificial intelligence (AI) has investigated the general intelligence capacity of machines within the domain of games more than any other domain given the ideal properties of games for that purpose: controlled yet interesting and computationally hard problems. This line of research, however, has so far focused solely on one specific way of which intelligence can be applied to games: playing them. In this paper, we build on the general game-playing paradigm and expand it to cater for all core AI tasks within a game design process. That includes general player experience and behavior modeling, general non-player character behavior, general AI-assisted tools, general level generation and complete game generation. The new scope for general general game AI beyond game-playing broadens the applicability and capacity of AI algorithms and our understanding of intelligence as tested in a creative domain that interweaves problem solving, art, and engineering.

Artificial Intelligence is the intelligence shown by machines or software. Artificial intelligence includes reasoning, natural processing language and even various algorithms are used to put the intelligence in the system. In this paper we investigate motivations and expectations for the development of Machine Intelligence. This paper also presents the role of ethics in developing artificial intelligence. In this paper we have compared the new emerging AI scope with the old technologies in various fields and its advantages to the society.

The content of artificial intelligence was to explain its specific concepts and provide examples it uses. Artificial intelligence aims at imitating human behaviour or creative human activity. Theory of artificial intelligence sets its goals. It is related to various scientific disciplines which complement it and in which it searches for possible solutions. Artificial intelligence is constantly discussed by scientists, philosophers as well as laics. They are all interested in two questions — what would happen without it and what it will bring along. The aim of artificial intelligence is to get to know, understand and fully grasp certain processes. These processes are considered manifestations of human intelligence.

This report will first review the breakthrough in the history of human intelligence. Seventy thousand years ago, Homo sapiens achieved “singularity” intellectual breakthrough, the key of this breakthrough is the language-based cognitive revolution they had achieved. Likewise, artificial intelligence also need a language cognitive revolution to achieve a breakthrough. How to break through cognitive intelligence? This report will introduce the breakthrough and initial results in cognitive intelligence IFLYTEK has achieved combined with the research plans, objectives of IFLYTEK Hyper Brain. At the same time, set forth how to use the “ripple effect” to achieve the core technology of artificial intelligence iterative optimization, in the background of the current big data, cloud computing, mobile Internet deeply applied, and to promote a higher level of artificial intelligence.

Due to the characteristics of web data and the change of men-machine interaction mode in the web age, artificial intelligence and information processing encounter new challenges and requirements. The new demand is that information processing has to deal with the meaning or semantics of information. Traditional information processing only treats the form of information rather than the meaning. Artificial intelligence intends to handle information as that of human beings, which makes machines to deal with the meaning or to understand the information. This talk presents new artificial intelligence technologies in the context of text and image processing, as well as intelligent robots. We will discuss how artificial intelligence may face the opportunity and challenge, and what strategy we will adopt to deal with them.

Representation has a fundamental role in Artificial Intelligence but there is still an open debate on basic issues on this subject. Particularly, there have been various studies on the emergence of communication and language in artificial agents, where the debate on representations underlying these processes should be significant, however not much discussion and studies have been done. We propose to identify and classify possible representational processes occurring during the emergence of communication, replicating a computational experiment previously proposed and evaluating neural network activations patterns. To define representation and its classes, including icons, indexes and symbols, we rely on the semiotics of Charles Sanders Peirce. Results show that symbolic associations are established during the evolution of artificial agents and such symbolic associations benefit adaptive success.

The modern school of Artificial Intelligence was originally expected to provide a full working model of intelligence as a set of procedures. Scholars implemented these procedures over time to conceptualize the notion of an intelligent machine. Computer scientists rushed to implement working models that would allegedly reach beyond many limits. Perhaps the most debilitating act was equating what is efficient in procedures to what is artificialized in intelligence. Equally debilitating was interpreting the speed of arithmetic calculations as a quantifier: it led to teams being interpreting speed and accuracy as reflections of intelligence. In order to reach an artificial form of intelligence that is faithful to the amalgam of biological, physical and chemical that it seeks to imitate; scholars of AI must reach a deeper synthesis of its integrative nature, leading to the creation of many artificial synthetic forms of Intelligence, instead of a single vision of intelligence that simply focuses on matching the performance of the human brain. Having said that, we can clearly concur that most of the AI Modern School's limitations have been discovered and are well-documented and known to the AI community. Our aim is to discuss a number of these issues, particularly the limits previously described. We avow that these limits emerged from epistemological misunderstandings on the perceived meanings of intelligence itself, leading to the limits imposed in the current interpretations of AI. Future work in AI, or alternatively coined Synthetic Intelligence, must revisit fundamental assumptions about the nature of the brain, cognition, computing, and intelligence. Synthetic Intelligence focuses on the phenomena such as intelligence and consciousness, and mapping them to the physics of the brain and models of brain processes at each of its multiple levels. It is the ‘stack’ of brain subsystems at multiple levels, from cortical down to molecular, joined by a common thread, that make up a mind. What we need are mathematically described mechanisms and information structures to integrate computational discourse analysis, value systems, mapping of cognitive structures to neuron interactions and to the molecular mechanisms of such interactions. The key to this discovery will be the study of emergence of intelligence and consciousness in engineered systems - implemented in silico or in vitro.

In this paper, I describe the Othello AI project completed during the 2014 summer Leap program at Carnegie Mellon University. I will first look at the basic components of two-player game AI. I will then look at basic properties of Othello AI design in the context of the project. Then I will examine the development of the Othello bot from the earliest to the latest versions. The performance of the bot relevant to humans and other bots will be analyzed and discussed. Finally I will look at future possible improvements to MyPlayer, the Othello Artificial Intelligence bot.

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.

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.

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.

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).

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.

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.

Artificial intelligence technology for non player character has become the key technology for computer games. This paper describes the attributes of non player character and application of artificial intelligence. Then it discusses the interaction designing of artificial intelligence in designing computer games. The latter part focuses on researching the realization of artificial intelligence for non player character. At last, the paper envisions the application of AI will attract more players.

Artificial intelligence is facing the changes of research program. New research program which is based on the idea of new connectionism should be established. This paper analyzes the dilemma of reductionism and holism existing in artificial intelligence theoretical research, and interprets theoretical basis of research program of new connectionism.

Research in automation focuses on systems which are capable of solving very complex tasks and problems. Artificial Intelligence and especially Cognitive Science have brought remarkable successes; however, in some areas the boarders of feasibility and further extension are reached. Compared to human intelligence the range of capabilities of the solutions is still modest. In the following we will argue why we see the necessity to introduce a novel approach for creating models, which possibilities and tools computer engineering can offer, why a psychoanalytical template is considered meaningful, and which open problems could be tackled or even broken through with this approach, respectively. The article is based on comprehensive research results in the course of several research projects including a European one. Involved persons originate from a number of research institutions in Austria, South Africa, and Canada.

Artificial Intelligence (AI), in its long journey since inception in 1956, has seen many cycles of successes and failures. It has undergone major focal transformations, both in terms of philosophical directions and the areas attracting generous funding. The journey of AI from Turing's test to current state of the art techniques, and their applications, can be seen as the A′B′C′D′ of Artificial Intelligence; with A to mean ‘Artificial’, B denoting ‘Builtin’, C standing for ‘Collective’ and D for ‘Derived’ Intelligence. In this paper, we have tried to track important defining paradigms of AI and demonstrate how Collective Intelligence, the new AI perspective, is enriching computational intelligence techniques, the World Wide Web (Web) and research in social sciences.

A method is presented to construct an embodied artificial intelligence. The method grows out of a system for object recognition in artificial vision and relies upon such a capability. Examples from the biosphere are extensively discussed in coming to the method which has, as its central tenet, that all intelligence is fundamentally related to objects. All the aspects needed by an embodied intelligence are developed including consciousness, memory and volition.

This paper reviews the field of artificial intelligence focusing on embodied artificial intelligence. It also considers models of artificial consciousness, agent-based artificial intelligence and the philosophical commentary on artificial intelligence. It concludes that there is almost no consensus nor formalism in the field and that the achievements of the field are meager.

Recent researches reveal that various paradigms of intelligence, such as natural, artificial, machinable, and computational intelligence, can be unified at the logical and functional levels known as abstract intelligence. This paper introduces abstract intelligence as a form of driving force that transfers information into knowledge and behaviors. An architectural framework of abstract intelligence and the generic abstract intelligence mode (GAIM) are formally developed that provide a unified theory for explaining the mechanisms of advanced intelligence. In order to deal with the highly complex and abstract objects in abstract intelligence, denotational mathematics is introduced as a category of expressive mathematical structures for modeling and manipulating high-level mathematical entities beyond numbers and sets, such as abstract objects, complex relations, behavioral information, abstract concepts, knowledge, processes, and systems. Applications of denotational mathematics in abstract intelligence, cognitive informatics, and computational intelligence are elaborated.

Summary form only given. When employing the views of granular computing to the studies of artificial intelligence, it can be seen that there have been various levels of granule existed, system level, element level, and sub-element level, etc., each of which has its own interest of research. As for the system level of granule is concerned, there have also been various views of angle among which structural angle (Structuralism Approach), functional angle (Functionalism Approach) and behavioral angle (Behaviorism Approach) are representatives. Each of the approaches to AI has made progresses and faced problems as well. In this paper, a new approach, named the Mechanism Approach, to the system level of granule in AI research is presented, which focuses on the core mechanism of intelligence formation in general cases. It is discovered that the Mechanism Approach can be implemented via the transformations that are able to converse the information to knowledge and further to intelligence. The new approach is also proven to be able to unify the three approaches mentioned above and thus bring about a unified theory of AI. Both the mechanism approach and the unified theory may make AI research more promising than ever before.

Artificial intelligence and physics are two components of 3D games. While they bring the laws of nature and more realism to games, they may be intensive enough to leave a sizable dent on the game 's performance and lead to a serious reduction in fame rate. In this paper, we discuss a game implementation solution with multi-core architectures which hides the computational load of the game intelligence and improves the gaming experience.

In this paper we propose a new approach to active video surveillance intelligence systems based on the integration of artificial neural networks (ANN) and symbolic Artificial Intelligence (AI). In particular, the neurosymbolic hybrid system here presented is formed by virtual neural sensors (WiSARD-like systems) and BDI agents. The coupling of virtual neural sensors with symbolic reasoning for interpreting their outputs, makes this approach both very light from the computational and hardware point of view and quite robust in performances.

Can machines think? This question has been an object of discussion and debate for over half-a-century. My interest in the question goes back to the beginning of my academic career. Officially, AI was born in l956. At its birth there was widespread expectation that within a few years it will be possible to build machines that could think like humans. I did not share that belief. To the pioneers, symbolic logic was all that was needed. Anything that involved numerical computations was unwelcome. It took close to thirty years for probability theory to gain grudging acceptance. Clearly, adding probability theory to the armamentarium of AI is a step in the right direction. But is it sufficient? In my view, the answer is: No.

Intelligence is a driving force or an ability to acquire and use knowledge and skills, or to inference in problem solving. This keynote lecture describes the taxonomy and nature of intelligence. It analyzes roles of information in the evolution of human intelligence, and the needs for logical abstraction in modeling the brain and natural intelligence. A formal model of intelligence is developed known as the generic intelligence mode (GIM), which provides a foundation to explain the mechanisms of advanced natural intelligence such as thinking, learning, and inferences. A measurement framework of intelligent capability of humans and systems is presented in the forms of intelligent quotient, intelligent equivalence, and intelligent metrics. On the basis of GIM model and theories, the compatibility of nature and machine intelligence is revealed, which forms a theoretical foundation for rigorous study in machine intelligence, AI, and intelligent systems.

Emotions were proven to play an important part in human intelligence. As a fellow of AI (artificial intelligence), there are many issues and argues in the area of artificial emotion (AE). An interesting new model is presented in a musician's point of view to solve the problems how machine can "apperceive", "develop" and "express" feelings emoting like human being. This model simulates the musical communications between musician (e.g. a composer and a pianist performing his works, or a conductor and his orchestra), which seem to be a "pure" emotional exchange.

We use the psychoanalytical model of the psychical apparatus to define a unified coherent model for intelligent bionic systems. The terms intelligence, feelings and emotions are central topics within the fields of psychology, pedagogy and psychoanalysis. When engineers use these terms, they have to consider the concepts of those scientific fields. Our heterogeneous team joining engineers and psychoanalysts attempts to map Sigmund Freud’s model of the ”psychical apparatus” in combination with Luria’s Dynamic Neuropsychology into a machine. Following up on the first paper of this forum which outlined the state-of-theart in Artificial Intelligence, this paper outlines the motivation of our new scientific step and describes visions and constraints we have encountered to date. Research results are presented in the following papers.

Western psychology has traditionally focused on processes considered to be internal or private to the individual, with the social world generally regarded as an aspect of "the environment." Recent cross-cultural psychological research reveals fundamental differences in the way cognition operates in people from different cultures, demonstrating that the social environment not only affects thought, but helps create it. These discoveries are mirrored in the field of computational intelligence; researchers identifying methods for eliciting intelligent behavior from machines are looking more and more into models that consider the individual inextricably integrated with the social milieu. These new models are radically different from traditional AI, which treats cognition as a set of processes taking place inside an isolated brain. In this lecture I will discuss these dichotomies in terms of the particle swarm algorithm, which is a model of collectively integrated intelligences; developments in the particle swarm paradigm will be framed in terms of the interplay of culture and cognition.

A fuzzy mechanism for building a goal-driven, life-purpose perspective for artificial intelligence, robotics or computational intelligence is presented in this paper. According to a non-traditional epistemological paradigm for understanding the term intelligence this adaptive fuzzy goal-driven approach becomes an alternative both to construct intelligent systems and to understand human intelligence. The fuzzy system as suggested may yet be seen as a useful mechanism for representing the approximate human-being reasoning due the likelihood it presents when compared to the way human beings adapt their reasoning and behavior in order to accommodate small changes caused by the environment or by the context. In this sense the proposed approach may also allow the nature of intelligent machine and intelligent human behavior converge to be the same.

Summary form only given. Intelligence science is a cross-discipline that dedicates to joint research on basic theory and technology of intelligence by brain science, cognitive science, artificial intelligence and others. Brain science explores the essence of brain, research on the principle and model of natural intelligence in molecular, cell and behavior level. Cognitive science studies human mental activity, such as perception, learning, memory, thinking, consciousness etc. In order to implement machine intelligence, Artificial intelligence attempts simulation, extension and expansion of human intelligence using artificial methodology and technology. The above three disciplines work together to explore new concept, new theory, new methodology. It will be successful and create a brilliant future in the 21 century. Brain science points out that perceptive lobes have special function separately, the occipital lobe processes the visual information, the temporal lobe processes auditory information, the parietal lobe processes the information from the somatic sensors. All of three lobes deal with information perceived from the physical world. Each lobe is covered with cortex where the bodies of neurons are located. Cortex consists of primary, intermediate and advanced areas at least. Information is processed in the primary area first, then is passed to intermediate and advanced areas. Comparing with computer system, the brain is the same as hardware and the mind looks like software. Most work in cognitive science assumes that the mind has mental representations analogous to computer data structures, and computational procedures similar to computational algorithms. Connectionists have proposed novel ideas to use neurons and their connections as inspirations for data structures, and neuron firing and spreading activation as inspirations for algorithms. Cognitive science then works with a complex 3-way analogy among the mind, the brain, and computers. Mind, brain, and computation can each be used to suggest new ideas about the others. There is no single computational model of mind, since different kinds of computers and programming approaches suggest different ways in which the mind might work. The mind contains perception, rational, consciousness and emotion. The long-term scientific goal of artificial intelligence is human-level intelligence. In this lecture, we will discuss basic research topics related to intelligence science, such as learning, memory, thought, language, consciousness etc. We also report the recent progresses containing: visual perception; introspective learning; linguistic cognition; consciousness model; and platform of agent-grid intelligence

Structuralism, functionalism as well as behaviorism have been the three major approaches to the Artificial Intelligence (AI) research in history up to the present time. These approaches have made great progress in AI so far. On the other hand, however, all the three are facing critical difficulties and lack of mutual understanding to each other. An Attempt was thus made in the paper to propose a different approach to the AI research, the cognitive approach that tries to explore in depth the cognitive mechanism of intelligence formation of intelligent systems. It is discovered, as result, that the cognitive mechanism of intelligence formation is a series of transformations that can converse the information to knowledge and further to intelligent strategy and the latter is the embodiment of intelligence in narrower sense. An interesting by-product is also found that the aforementioned three approaches appear to be harmoniously unified within the framework of the cognitive approach. A brief report will be given here in the paper on the cognitive approach as well as the unification of the existed three AI approaches.

The characteristics of human mind are summarized up after the structure, functions and the working procedure of the eight consciousness and the human knowledge structure in consciousness-only psychology have been introduced. The concept of super-languages is put forward after the elements of human intelligence simulated with computers and the development tendency of programming languages have been analyzed. It is concluded that processing the formalized partial information is the limit of artificial intelligence

The frame problem, originally proposed within AI, has grown to be a fundamental stumbling block for building intelligent agents and modeling the mind. The source of the frame problem stems from the nature of symbolic processing. Unfortunately, connectionist approaches have long been criticized as having weaker representational capabilities than symbolic systems so have not been considered by many. The equivalence between the representational power of symbolic systems and connectionist architectures is redressed through neural manifolds, and reveals an associated frame problem. Working within the construct of neural manifolds, the frame problem is solved through the use of contextual reinforcement learning, a new paradigm recently proposed.

A definition and structure of intelligence is suggested based on research results in neurophysiology, psychology, and system theory. Intelligence is defined as the ability to solve problems using space and time resources. The problem solving behavior depends on experience, innovation, solution fusion, and learning mechanisms. Suggestions for implementing these proposed components are provided and discussed, as are cross-disciplinary research results, which support the proposed structure and suggestions for implementation. Implications of the cross-disciplinary structured intelligence (1) on research on artificial intelligence systems such as human-machine systems and on simulation models as well as (2) on intelligence testing are brought into account.

In this paper, we explore the interdependent roles of computational intelligence, data mining, and machine learning. We explain how these three seemingly independent AI specialties are inextricably linked through the science of randomization and reuse. Computational intelligence is shown to imply soft computing, whose bounds are soft or fuzzy only because they need be adoptively formed (e.g., type II fuzzy logic). Data mining implies the compression of data (including anomalous data) to its simplest form. Such randomization operations are inherently incomplete and thus are necessarily domain-specific or adaptive as a consequence. Finally, machine learning is the study of computational adaptation. For example, a machine that plays master-level chess is not necessarily computationally intelligent - unless it is capable of adaptive improvement in its play. This paper will attempt to make it clear that intelligence and the capability for learning are in fact synonyms for an AI.

Logical omniscience is usually thought as a big problem faced by standard modal or epistemic logic study, or its application in areas like distributed artificial intelligence research. However, by arguing with the question how or under what senses it's raised as a problem, we find this problem is just declared unclearly, or even improperly. That is, in some sense, it's just a problem given by some scholars fundamental-confusedly and so invalidly. Hence, if there's some problem there, it must be some problem else, but definitely not that logical omniscience problem, because there are no proper grounds for us to declare this problem upon.

After the problems in the research of artificial intelligence have been analyzed, we have discussed the nature and characteristics of human intelligence from the view of the consciousness-only psychology and put forward AORBCO model which is a formalistic model of intelligence. The nature expressed with the form and the form which the nature is expressed with can been united in AORBCO model and the intelligent models in the three schools of artificial intelligence can been integrated in AORBCO model.

The article develops an ontological view for analysis of intelligent systems. The idea is to view intelligent systems as series of ontological transformations of information. The paper, then, proposes and analyses the claim that the assumption of fixed ontology significantly bounds the abilities of an intelligent system, especially their performance in complex environments. This limiting assumption is used in most of current AI designs.

We present a paradigm that can contribute with some essential aspects to establish a baseline for the development of computational sapience (wisdom), sapient (wise) decision/control, and sapient (wise) systems as new disciplines/fields. It is demonstrated here that, under the proposed paradigm, computational sapience (wisdom), sapient (wise) decision/control, and sapient (wise) systems methodologies can be developed as a natural extensions of some knowledge intensive (artificial/computational intelligence, soft computing) approaches. In particular, the proposed paradigm and general framework is an attempt to the formalization of these novel methodologies.