Control of complex distributed systems with distributed intelligent agents

Control of spatially distributed systems is a challenging problem because of their complex nature, nonlinearity, and generally high order. The lack of accurate and computationally efficient model-based techniques for large, spatially distributed systems leads to challenges in controlling the system. Agent-based control structures provide a powerful tool to manage distributed systems by utilizing (organizing) local and global information obtained from the system. A hierarchical, agent-based system with local and global controller agents is developed to control networks of interconnected chemical reactors (CSTRs). The global controller agent dynamically updates local controller agent’s objectives as the reactor network conditions change. One challenge posed is control of the spatial distribution of autocatalytic species in a network of reactors hosting multiple species. The multi-agent control system is able to intelligently manipulate the network flow rates such that the desired spatial distribution of species is achieved. Furthermore, the robustness and flexibility of the agent-based control system is illustrated through examples of disturbance rejection and scalability with respect to the size of the network.     

Accuracy of intelligent medical systems

Intelligent medical systems are a special kind of medical software in general, and just as any medical software system they should make accurate presumptions. However, accuracy of intelligent medical systems is highly dependent on various factors such as: choosing an appropriate basic method (i.e. decision trees, neural networks), induction method (i.e. purity measures) and appropriate support methods (i.e. discretization, pruning, boosting). In this paper we present the results of extensive research of the above alternatives on 54 UCI databases and their influence on the accuracy of decision trees, which constitute one of the most desirable forms of intelligent medical systems. We also introduce new hybrid purity measures that on some databases outperform other purity measures. The results presented here show that the selection of the right purity measure with the proper discretization method and application of the boosting method can really make a difference in terms of higher accuracy of induced decision trees. Thereafter choosing the appropriate factors that can increase the accuracy of the induced decision tree is a very demanding and time-consuming task.     

RHODES to intelligent transportation systems

To help fulfill the promises of ITS (intelligent transportation system), the ATLAS (Advanced Traffic and Logistics Algorithms and Systems) research center is developing and testing the RHODES (real-time hierarchical optimized distributed effective system) traffic control system. We believe that RHODES play a major role in the realization of future Advanced Traffic Management Systems, a major component of ITS.     

Validation method for intelligent systems

Knowledge incorporated intelligent solving methods are prevailing in practical planning and scheduling systems because of the large problem size and complex constraints. However, when intelligent methods are used in real applications, it is an important problem to validate that both optimality and response time are at expert-level. Turing Test approaches are useful in validating intelligent systems. Nevertheless, validation burdens experts with heavy tasks. In distribution route scheduling, for instance, validation load is considerable since patterns of distribution spots are numerous. A bi-directional, many-sided explanation typed multi-step validation method, which enables to share validation tasks among experts, KEs (Knowledge Engineers) and computers, is proposed to diminish the load for busy experts, in validating intelligent systems. Furthermore, an additional validation tool based on the above method is also proposed. This method ensures efficient validation due to the decrease of load for experts by means of sharing validation tasks through multi-step validation with experts, KEs and computers. From this viewpoint, the proposed method can be used in validating general intelligent solving methods and intelligent systems, as well as intelligent scheduling systems.     

The design of intelligent socio-technical systems

The design of intelligent socio-technical systems calls for careful examination of relevant social and organizational concepts. We present a method for supporting this design process, placing emphasis on different levels of formal characterization, with equal attention to both the analysis of concepts in a formal calculus independent of computational concerns, and the representation of concepts in a machine-processable form, fully cognizant of implementation issues—a step in the method we refer to as principled operationalization. There are many tools (i.e. formal languages) that can be used to support the design method; we define and discuss criteria for evaluating such tools. We believe that, were the method proposed to be adopted, it would enhance the state-of-the-art in the systematic design and engineering of socio-technical systems, respecting the fundamentally interdisciplinary nature of those tasks, in both their theoretical and practical dimensions.     

Principles of intelligent learning systems design

One of the possible system approaches to the construction of artificial intelligence (AI) systems is described in this paper. The approach integrates three well-known AI trends: heuristic programming, structural modelling and simulated evolution. The structure of an experimental learning system ELS Y is described. The system is designed in accordance with the proposed principles. The main feature of the system is a dynamic generation of AIS architectures on the basis of means-ends analysis method. An AIS architecture can be subjected to mutation in order to obtain a more intelligent one. Every AI system is capable of self-organization on two levels: the first one formed with an associative computing memory, and the second one with arrangement of knowledge structures.     

Paraconsistent intelligent systems of computer logics

The paper considers applicative computation systems (ACS) constructed as Gentzen-deductive extensions of Church's -conversion calculi with a bounded cut rule and as Sheinfinkel'-Curry pure combinatory logic.Translated from Kibernetika, No. 1, pp. 9–12, 34, January–February, 1991.     

Formal reasoning in intelligent database systems

Expert database systems were proposed to solve the difficulties encountered in traditional database systems. Prolog provides a fast prototyping tool for building such database systems. However, an intelligent database system implemented in Prolog faces a major restriction that only Horn rules are allowed in the knowledge base. We propose a theorem prover which can make inference for non-Horn intelligent database systems. Conclusions can be deduced from the facts and rules stored in a knowledge base. For a knowledge base with a finite domain, the prover can provide correct answers to queries, derive logical consequences of the database, and provide help in detecting inconsistencies or locating bugs in the database. The theorem prover is efficient in deriving conclusions from large knowledge bases which might swamp most of the other deductive systems. The theorem prover is also useful in solving heuristically the satisfiability problem related to a database with an infinite domain. A truth maintenance mechanism is provided to help eliminate repetitious work for the same goals.Supported by National Science Council under grant NSC 81-0408-E-110-9.     

Integration strategy for distributed intelligent systems

In this article, we describe a new framework for designing real-time intelligent control systems. An integrated intelligent system is a large knowledge integration environment that consists of both symbolic reasoning systems (expert systems) and numerical computation packages. These modular software programs are controlled by a meta-system which manages the selection, operation, and communication of these programs. This new architecture can serve as a universal configuration to develop high-performance intelligent systems for many complicated application domains in the real-time manufacturing process. As an example, an intelligent optimal control is utilized to illustrate the integrated intelligent control system.     

An automata-theoretic framework for intelligent systems

A theoretical framework for the analysis and synthesis of intelligent systems is presented. The basic models of automata theory and adaptive systems are highlighted, then extended and interpreted in the context of intelligent system design. In particular, the basic models are generalized to intelligent systems incorporating self-learning behavior. The utility of this approach is demonstrated by its use as a rigorous basis for (1) the discussion of information minimization in automatic systems, (2) the formal structure of adaptive systems and (3) implications for a general theory of intelligent systems.     

Knowledge framework for intelligent manufacturing systems

Nowadays, competition is experienced not only among companies but among global supply chains and business networks. There is a demand for intelligent world-class solutions capable of reinforcing partnerships and collaborations with an improved cross-cultural understanding. However due to the proliferation of terminology, organizations from similar business environments have trouble cooperating, and are experiencing difficulties exchanging electronically vital information, such as product and manufacturing data, even when using international standards. To address similar interoperability problems, the Intelligent manufacturing systems program () is providing an opportunity to develop industry-led R&D initiatives, building common semantics and integrated solutions. The SMART-fm project was one of those initiatives. It led to the development of the international standard for product data representation and exchange in the furniture sector (ISO 10303-236) and identified the challenge of semantic interoperability which is today a major challenge in modern enterprise integration. This paper presents a knowledge framework to address that challenge and make interoperable intelligent manufacturing systems a reality. It proposes to use semantically enriched international product data standards, and knowledge representation elements as a basis for achieving seamless enterprise interoperability.     

Tracking systems for intelligent road vehicles

The paper describes two mono-target tracking systems based on the control of a mixed camera/3D sensor. The 3D sensor is a laser range finder. Both systems are presented in an intelligent road vehicle context, for the purpose of obstacle detection. The first is applicable to the pedestrian tracking when the vehicle does not move. The data are obtained by a 3D segmentation process. The second tracking is used for mobile vehicle tracking. In this application, the experimental vehicle is moving, and this tracking is multisensorial, which means that it makes the most of the complementarity of both 3D and intensity data. In that case, two kinds of data are used: the distance of the obstacle obtained by a visual servoing, and the obstacle location in the intensity image given by an image processing. This tracking also involves space and temporal data alignments, which are necessary steps before any data fusion. The data combination is done by an extended Kalman filter.     

Competence and responsibility in intelligent systems

The capabilities of Artificial Intelligence have increased dramatically over the last decade. We can now contemplate the use of automation in tasks which were previously considered the exclusive domain of human professionals. Such a possibility raises new legal issues. This paper argues the need for finding methods of assessing the competence of such systems in order to assign responsibility for their actions.     

Student modelling in intelligent tutoring systems

Student modelling is a special type of user modelling which is relevant to the adaptability of intelligent tutoring systems. This paper reviews the basic techniques which have been used in student modelling and discusses issues and approaches of current interest. The role of a student model in a tutoring system and methods for representing information about students are discussed. The paper concludes with an overview of some unresolved issues and problems in student modelling.Electric Brain Company     

Conditioning and inference in intelligent systems

 We maintain that among the research trends concerning the various aspects and methodologies for the management of partial and revisable information in automated reasoning and giving particular emphasis to conditioning and inference, conditional events and conditional probability (in a coherent– in the sense of de Finetti – framework) play a central role: we will review some of our and related results, showing that this approach is able to encompass many of other existing numerical and symbolic methods dealing with the treatment of partial knowledge and uncertainty in Artificial Intelligence.     

SI: methodologies for intelligent systems

Journal of Intelligent Information Systems -