Artificial Intelligence(AI); The Future of Everything.

Artificial Intelligence(AI); The Future of Everything. 1

Artificial Intelligence(AI); The Future of Everything. 2Artificial intelligence is the collection of computations that at any time make it possible to assist users to
perceive, reason, and act. Since it is computations that make up AI, the functions of perceiving, reasoning, and
acting can be accomplished under the control of the computational device (e.g., computers or robotics)
AI at a minimum includes:
• Representations of “reality,” cognition, and information, along with associated methods of representation.
• Machine learning;
• Representations of vision and language;
• Robotics; and
• Virtual reality (defined below)

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Human-computer interface (HCI) consists of the following:
• The machine integration and interpretation of data and their presentation in a form convenient to the
human operator or user (i.e., displays, human intelligence emulated in computational devices, and
simulation and synthetic environments).
• The bidirectional communication of information between two powerful information processors: people and computers. Information can be in the form of data, symbolic
knowledge, or control specifics.

                                                                                     Virtual Worlds

A virtual world, or virtual reality, is a precise re-creation of a real-world environment via multisensory data
and computer graphics that allows interaction between humans and synthesized objects. It consists of a set of
multisensory devices employed as both actuators and effectors. “Virtual” is often used synonymously with
computer-generated or synthetic.
                                                                          Synthetic Environments

A synthetic environment is a reconstructed multipurpose environment with a mix of real and computersynthesized (simulated) objects under computer control. It allows interaction between combinations of real and
synthesized objects. A synthetic environment consists of a digital and analog representation of a physical
environment with specified fidelity and complexity and is scalable to any size and degree of complexity.

                                                                    GRAND CHALLENGE AREAS

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The grand challenge areas selected by the panel should be understood in the context of the usage of that
term in the High Performance Computing and Communications (HPCC) 1991 Initiative of Alan Bromley,
President Bush’s Science Advisor, stated as follows: “The HPCC Program is driven by the recognition that
unprecedented computational power and capability is needed to investigate and understand a wide range of
scientific and engineering grand challenge problems. These are fundamental problems whose solution is critical
to national needs.”
The panel tailored this concept to its work in the following manner: Grand challenge areas are those
fundamental problem areas to which the application of scientific and engineering resources will yield muchneeded improvements in capabilities and performance. They also serve to identify key scientific and engineering
issues and opportunities.
In selecting the grand challenge areas, the panel applied the following two constraints:
• Only leading-edge technologies were considered, and
• Research and technology application communities had to believe that the grand challenges were
susceptible to resolution and that their resolution would provide demonstrable value-added to nontrivial
user groups.

• Representation and modeling of complex systems,
• Collaborative problem solving,
• Machine learning and adaptive systems,
• Reasoning under uncertainty,
• Virtual worlds (reality), and
• Neurophysiological models of cognition.

REPRESENTATION AND MODELING OF COMPLEX SYSTEMS

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Prior to the advent of the computer, traditional engineered systems (e.g., bridges, airplanes, and ships) were
modeled by a mathematical formalism combined with a set of engineering heuristics for how to apply the theory
to given situations. Today, we are faced with the design, construction, maintenance, and use of much more
complex systems. They require representations of the problem that go beyond systems of equations and
modeling techniques and require more than closed-form solving, numerical approximation, or numerical
simulation. The dimensions of this complexity can be exemplified as follows:
• A system today is a heterogeneous system of subsystems, so that no one representation and modeling
paradigm suffices.
• A system is typically controlled by a software-based supervisory system that is usually the single most
complex subsystem and is best modeled in detail by discrete, logical models rather than continuous,
physical ones.
• One or more human users are part of the overall system and require interactions with the other
subsystems to allow for monitoring and control.
The grand challenge is to be able to model entirely in software a range of complex systems. Two specific
application milestones that would validate this achievement are the following:
• The ability to design and evaluate in software a sophisticated new weapon system, including carrying
platform, sensors, weapons, communications, control systems, and human decision makers. This
“prototype before build” capability (smaller-scale examples of which include Boeing’s use of computeraided design (CAD), computer-aided software engineering.

(CASE), and computer-aided manufacturing (CAM) in the design of the 777) would help the Navy to
stay even with rapidly changing threats, technologies, priorities, and budgets.
• The building of a fully autonomous undersea or air vehicle to perform one mission with reasonable
generality.
                                                                               Research Objectives

Short term—Integration of discrete, continuous, and symbolic representations of models.
Midterm—Validation of internal consistency and completeness and against external specifications;
representation and explanation of complex models to humans; and computation-constrained,
satisfactorily approximate solutions based on dynamic, variable-resolution models controlled by
metamodels.
Long term—Global optimization across complex models; and very high level languages for system
design (including decision-support software) and theory of model design.
Ongoing Leading-Edge Activities and Organizations

                                                                           Needed Resources

The necessary ingredients for research in this area are summarized below:
• Skills—Expert systems, simulation and modeling, automated software design and synthesis, computeraided design, and human physiology.
• Facilities—Major computational resources to execute and validate complex models: distributed
workstations plus parallel computer available over the network could suffice.
• Cooperation opportunities—Multiple groups must collaborate to model complex systems (e.g., a ship)
because the expertise never resides totally in one organization. Two or more research groups could
collaborate by modeling different subsystems of a problem.
• Level of effort—For small, theoretical tasks, one person could make progress; large, demonstrationoriented tasks would require a group of at least five, for example, one engineering system researcher,
one simulation/modeling researcher, one application expert, one knowledge engineer, and one
programmer.

Artificial Intelligence(AI); The Future of Everything. 6

In recent years, with the advancement of artificial intelligence (AI) and information science and technology, there has been a resurgence of work in combining individual intelligent systems (knowledge-based systems, fuzzy logic, neural networks, genetic algorithms, case-based reasoning, machine learning and knowledge discovery, data mining algorithms, intelligent agents, soft computing, user intelligent interfaces, etc.) into integrated intelligent systems to solve complex problems. Hybridization of different intelligent systems is an innovative approach to construct computationally intelligent systems consisting of artificial neural network, inference systems, approximate reasoning and derivative free optimization methods such as evolutionary computation and so on. The integration of different learning and adaptation techniques, to overcome individual limitations and achieve synergetic effects through hybridization or fusion of these techniques, has contributed to a large number of new intelligent system designs. Hybrid intelligent systems are becoming a very important problem solving methodology affecting researchers and practitioners in areas ranging from science, technology, business and commerce. Specifically, there have been many attempts to solve decision making problems (assessment or evaluation and selection) by applying neural network and expert systems techniques.

The capabilities of rule-based expert systems are inherently well suited for decision making problems. The major drawback, however, is that the programmer is required to define the functions underlying the multi-valued or ranked possibility optimization. Furthermore, expert-type rules use a comprehensive language system that may have built-in biases, embedded goals, and hidden information structures, which may result in errors. Neural networks using mathematical relationships and mappings to design and optimize systems are capable of statistical decision-making given incomplete and uncertain information, and can be used to adapt to the user/designer’s requirements. Unlike rule-based expert systems, they evaluate all the conflict constraints or fusion information simultaneously, and model/learn the knowledge base using black-box techniques. They do not use rules in the formal sense so the evaluation or decision making time can be greatly reduced from that of rule-based modeling. The strengths of neural networks accrue from the fact that they need not priori assumptions of models and from their capability to infer complex, nonlinear underlying relationships. From the statisticians’ point of view, neural networks are essentially statistical devices to perform inductive inference and are analogous to non-parametric, nonlinear regression models.

However, existing neural schemes use two or more separate neural networks to accomplish some tasks respectively, and need to train them separately. This is tedious and costly, and sometimes very difficult. In order to overcome the suffered shortcomings or difficulties above, more research endeavors are necessary to develop more general topologies of neural models, learning algorithms and approximation theories so that those models are applicable in the system modeling and control of complex systems. A new kind of hybrid neural networks is therefore required for decision support. It must also be conceded that rule-based expert systems are much easier for humans to error-check than an ensemble of continuous equations in neural networks. In view of these practical requirements and current research status and future trend of intelligent decision support, an evolutionary fuzzy neural network (FNN) model has been developed for supporting computational intelligent decision making and simulation. There is now a growing need in the intelligent community that complex decision making problems require hybrid solutions. It is well known that intelligent systems, which can provide human-like expertise such as domain knowledge, uncertain reasoning, and adaptation to a noisy and time-varying environment, are important in tackling practical computing problems. Soft computing is an emerging collection of computing methodologies to exploit tolerance for uncertainty, imprecision and partial truth to achieve useable robustness, tractability, low total cost and approximate solutions. It is particularly efficient and effective for NP-hard problems.

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Most work in AI has focused on components of the overall system — such as learning, planning, knowledge representation, perception, and action. Information system/technology and intelligent knowledge management are playing an increasing role in business, science and technology. The breadth of the major application areas of intelligent, knowledge-based systems, and integrated intelligent information systems technologies is very impressive. These include, among other areas: agriculture, business, chemistry, communications, computer systems, education, electronics, engineering, environment, geology, image processing, information management, law, manufacturing, mathematics, medicine, metrology, military, mining, power systems, science, space technology, and transportation.

Reflecting the most fascinating AI-based research and its broad practical applications, integrated intelligent information system (IIIS) technologies, with the extensive use of the AI and information technology and their integration, are being utilized to advance engineering technology, increase manufacturing productivity, and improve medical care, as well as play a significant role in a very wide variety of other areas of activity with substantive significance. Integrated intelligent information systems (IIIS) are gaining better acceptance both in academia and in industry. The driving force behind this is that integrated and hybrid intelligence and distributed 3C (collaboration, cooperation, and coordination) allow the capture of human knowledge and the application of it to achieve high quality applications. Further motivation arises from steady advances in individual and hybrid intelligent-systems techniques, and the widespread availability of computing resources and communications capability through the world-wide web. However, the difficulties in distributed & integrated information systems development are increased due to the issues of intra- and inter- enterprise network communication, system heterogeneity, and information security, different engineering data formats and database formats. Many kinds of distributed information systems have been designed and implemented to address those difficulties in DIS development, which can provide an “information pipeline” that supports the sharing of information, specifically, in the context of collaborative/cooperative engineering. These systems are based heavily on industry standards (e.g., STEP, Standard for the exchange of product model data) to provide an open and evolvable environment that can flow with, as well as contribute to, commercial best practices and trends.

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The importance of integrating knowledge engineering (KE) practices into domains has been acknowledged by a number of researchers. Earlier studies focused on integrating reasoning mechanisms in domain specific support systems, mainly as diagnostic tools. Gradually, the research focus has shifted from the reasoning mechanism to the knowledge base. Recent studies focus on system and/or task structures, unified data and knowledge modeling and representation (e.g., UML/SysML), data management, standards, etc., concentrating on how these are integrated. Latest research indicates a trend towards semantic data and knowledge modeling for better interoperability. However, although these studies address the use of KE practices in the domains and some of them followed a uniform approach towards the development of knowledge bases, few of them can provide ontology-based knowledge management and reasoning services. As a result, a major limitation of the previous work is the lack of reusability and interoperability for common services. This challenge could be addressed if knowledge modeling were unified, formalized and enriched by employing ontological principles and semantics.

 

Ontology and ontology-based computational services will be able to provide new kinds of knowledge management and reasoning services that facilitate the sharing and reuse of data and knowledge across various phases of system development. The current research focus is in developing formal ontologies with emerging semantic methods such as Process Specification Language (PSL), Web Ontology Language (OWL) and resource description framework (RDF), in support of these integration scenarios. This work will advance the research on ontology-based knowledge service, and is a step towards formalizing support system as a knowledge intensive system. Topics and related issues of artificial intelligence, soft computing, and integrated intelligent information systems include but are not limited to the following:

Artificial Intelligence(AI); The Future of Everything. 9
1. Foundations and principles of data, information, and knowledge models

2. Methodologies for IIIS analysis, design, implementation, validation, maintenance and evolution

3. User models, intelligent and cooperative query languages and interfaces

4. Knowledge representation and ontologies, integration, fusion, interchange and evolution

5. Intelligent databases, object-oriented, extended-relational, logic-based, active databases, and constraint management

6. Intelligent information retrieval, digital libraries, and networked information retrieval

7. Distributed multimedia and hypermedia information space design, implementation and navigation

8. Visual interfaces, visual query languages, and visual expressiveness of IIIS

9. Machine learning, knowledge discovery, and data mining

10. Soft computing (including neural nets, fuzzy logic, evolutionary computing, probabilistic reasoning, and rough set theory) and hybrid intelligent systems

11. Uncertainty management and reasoning under uncertainty

12. Intelligent integration of information, information and knowledge repository

13. Distributed intelligent information systems, cooperative information systems, agent architectures and systems (including multi-agent scenarios)

14. Information and knowledge grid, grid computing, grid services for distributed systems integration

15. Ubiquitous computing, ambient intelligence, heterogeneous intelligent information systems interoperability

16. Industrial informatics, i.e., applications and case studies in novel applications, e.g., scientific databases, e-commerce, e-logistics, engineering design and manufacturing, product life cycle management and knowledge management, healthcare, education, etc.

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