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The next paper is titled I "Adding Interpretability to Neural Knowledge DNA". i It addresses in a novel way one of the biggest challenges of Artificial Intelligence - explainability. It is an alternative conceptualization of artificial intelligence (AI) that focuses on AI's assistive role, emphasizing the fact that cognitive technology is designed to enhance human intelligence rather than simply replacing it. The Authors introduce the decision tree-based generative method for knowledge extraction and representation to make the NK-DNA explainable, which is a big step toward trust in Artificial Intelligence (AI). [Extracted from the article]

Cyberattacks are becoming more and more intense in the banking industry (Ryzhkova et al. 2020). Banking industry is trying to adopt artificial intelligence to create cyber defence system so that the unauthorized access and cyber attacks would be minimized. Banks in Qatar acknowledge the threat of cybercrimes and the role of cybersecurity in sustainable growth. Meanwhile, the banking industry is experiencing a major technological disruption. It becomes important to understand the implications of such technologies as artificial intelligence (AI) on the cybersecurity of banks. The present paper aims to explore the impacts of AI on the cybersecurity of banks in Qatar. A thematic analysis of interviews of 9 experts in the banking industry of Qatar was conducted. A qualitative thematic analysis was conducted using NVIVO 12 tool. Four key themes were identified: (1) AI is a major tool for enhancing the cybersecurity of banks in Qatar; (2) banks face challenges in using AI for improving cybersecurity; (3) AI can be used destructively and thus poses a threat to cybersecurity in banks of Qatar; (4) employed AI-based tools have vulnerabilities that can be exploited. Overall, Qatar banks can be expected to face new challenges in the future, due to changes in regulatory frameworks and increasing availability of AI-powered malware. [ABSTRACT FROM AUTHOR]

Cybernetics is many different things to many different people and thus it has been used within various scientific disciplines. The aim of the presented paper was to produce a snapshot of cybernetics information source production using bibliometric analysis. The results showed that the production of information sources has been increasing, with a significant but temporary decrease in 90 s. The distribution in the more prolific countries and institutions is similar to other scientific fields, with the exception of China, which is higher ranked than usual. Important cybernetic papers are published in high-ranking journals. The more frequent topics covered are engineering and computer sciences. Cybernetic research in 2013 was mostly funded by Australasian, U.S., and European funding agencies. [ABSTRACT FROM AUTHOR]

The satisfactory results validate the proposed application of classifiers on the dataset improved by data-balancing techniques. Several experiments on a benchmark dataset of household electric energy demand show that the proposed method explains the prediction appropriately with the most influential input attributes in the long-term and short-term dependencies. Novel solutions, based on soft-computing techniques, are proposed in the present issue. [Extracted from the article]

Electricity generation from burning fossil fuel is one of the major contributors to global warming. Renewable energy sources are a viable alternative to produce electrical energy and to reduce the emission from power industry. They have unlocked opportunities for consumers to produce electricity locally and use it on-site that reduces dependency on centralized generation. Despite the widespread availability, one of the major challenges is to understand their characteristics in a more informative way. Wind energy is highly dependent on the intermittent wind speed profile. This paper proposes the prediction of wind speed that simplifies wind farm planning and feasibility study. Twelve artificial intelligence algorithms were used for wind speed prediction from collected meteorological parameters. The model performances were compared to determine the wind speed prediction accuracy and model comparison for different sizes of data set. The results show, the most effective algorithm varies based on the data size. [ABSTRACT FROM AUTHOR]

RPA is used to complete repetitive tasks usually completed by humans. A robot is a software programed to mimic the operations of humans on computer systems. Robots are configured to accomplish these tasks using algorithms from the machine learning and artificial intelligence field. Using RPA leads to reduced errors in the processing of tasks and reduced completions times to carry out the tasks. This paper calculates the number of software licenses required to carry out thousands of transactions for financial services and proposes an assignment of the transactions to the robots. A two-phase linear integer optimization process is proposed. The first phase calculates the number of robots required to complete all of the tasks and the second phase assigns the tasks to the robots. Many different transaction types for financial services are to be completed, and each type has precise market operating hours, maximal delays in their treatment, different processing times and different volumes. The objective function minimizes the total number of robots, but also limits the number of robot startups and reconfigurations, since these operations take time to complete. The key behind the fast computing times to solve the problem is the formulation, where periods are defined at intersections of market operating hours. Preliminary tests are conducted on a real case provided by a bank in North America and a synthetic test case and show that the problem is solved quickly. [ABSTRACT FROM AUTHOR]

Is the intelligence of a collective larger than the intelligence of its members? How does one determine the knowledge of a collective on the basis of the knowledge of its members? In this paper, we try to answer these questions. Many examples show that the knowledge of a collective is not a usual union of the knowledge of its members. If we assume that the members of a collective possess their knowledge states about some common real world, and these states reflect to some degree the proper (real) state of the knowledge about the real world, then a question arises: How does one determine the knowledge of the collective, and what is its quality? In this paper, we use Consensus Theory to solve this problem and to show that, in many cases, the collective knowledge state is more proper than the knowledge states of the collective intelligence. [ABSTRACT FROM AUTHOR]

Artificial intelligence for law is a thriving area of research as of June 2003. The article discusses contributions made by different authors in the field of artificial intelligence. Artificial intelligence as such has a wide scope and usability in wide spectrum.

Recently, in various information areas including image processing and social networks, data clustering has been vastly utilized. Thus, the clustering task is taken as successful while imputing the quality of uncertain values, whereas the traditional methods observe only poor performance. To enhance the performance, the hybrid intelligent-based clustering model is proposed by the adaptive concept. Initially, the data is collected from benchmark datasets that are composed of incomplete mixed data. The first level of clustering is processed by optimized K-Means Clustering (KMC) to acquire the optimal centroid, in which the centroid is optimized by the Adaptive Probability-assisted Water Strider Algorithm (AP-WSA). Similarly, the second clustering is done through K-Correlation-based Clustering (KCC) to obtain the clustered output. Thus, the final clustering process is named as Hybrid K-Means K-Correlation (HKMKC) clustering algorithm. Finally, the experimental results are validated with the performance measures and estimated with the traditional methods. Through the result analysis, the Average Silhouette Coefficient (AS) of the designed AP-WSA-HKMKC is 50%, 28%, 16%, and 5.45% progressed than KMC, FCM, KCC, and K-median clustering methods for dataset 2 at 15% miss rate. Thus, the designed hybrid clustering ensures the efficiency of handling incomplete data regarding accurate clustered outputs. [ABSTRACT FROM AUTHOR]

Modern knowledge representation is a very dynamic domain because of continuous research and development. This paper presents Logical Petri Nets (LPNs) and Fuzzy Petri Nets (FPNs) as models for knowledge representation. It is shown that knowledge propagation, described using logical and fuzzy Petri nets, terminates in a unique stable state. Based on this result, the paper introduces an algorithm for knowledge propagation in decision support systems. [ABSTRACT FROM AUTHOR]

Editorial. Introduces the articles featured in the October 2001 issue of the periodical 'Cybernetics and Systems: An International Journal.' Application of artificial intelligence methodologies and related soft technologies to the management process addressing the whole life cycle of products and services; Overview of the tools used to support decisions at various stages of the enhancement process.

Conflict is an omnipresent phenomenon in human society. It spans from individual decision-making trade-offs such as deciding what to do next (sleep, eat, work, play), to complex scenarios including politics and business. The social sciences, psychology, economy, and biology study the nature of conflict, its consequences, and strategies to successfully deal with it. Over the last decades computer science has joined those disciplines and studies conflict from a computational perspective. This special issue presents a selection of the best papers presented at the First Workshop of Conflict Resolution in Decision Making (COREDEMA). The workshop focused on computational approaches that tackle conflict in order to provide new insights and explore potential applications. The workshop was jointly hosted with the 12th International Conference on Practical Applications of Agents and Multi-Agent Systems (PAAMS) in Salamanca, Spain, from June 4 to 6, 2013. [ABSTRACT FROM PUBLISHER]

The objective of this paper is to present a complete conception of decision support systems for software project management. The idea is founded on dynamic and self-adjusting fuzzy models. Specifically, the propsed approach is founded on the knowledge-based methodology and the theories of dynamic systems and fuzzy sets. The presented method can be used in manufacturing of knowledge-based Information Technology (IT) products. A preliminary design of a fuzzy model can take advantage of experience from managing projects. Relevant experimental data resulting from such projects can then be utilized for adjusting the membership functions of the fuzzy model applied in the knowledge-based decistion-support system. [ABSTRACT FROM AUTHOR]

The need for intelligent systems has grown in the past decade because of the increasing demand on humans and machines to perform better. The researchers of artificial intelligence (AI) have responded to these needs with the development of intelligent hybrid systems. This paper describes the modeling language for interacting hybrid systems in which we will build a new hybrid model of cellular automata and multiagent technology. Simulations with complex behavior will be model social dynamics where the focus is on the emergence of properties of local interactions. Therefore, in our approach, cellular automata form a useful framework for the multiagent simulation model and the model will be used for traffic system which lies in coordinating the local behavior of individual agent to provide an appropriate system-level behavior in grid of interacting organisms. [ABSTRACT FROM AUTHOR]

This paper first briefly presents the environment and challenges that companies wilt encounter in the current decade, enhancing innovative society and business style. On this basis, the authors investigate the challenges that will impact the implementation of human resources management in companies as a cybernetic system. Because there exists a two-way link between the companies and the environment, this contribution also illustrates the link in the other direction and shows how a company can gain competitive advantage by effective human resources management. The point is that companies are moving from being knowledge-based to being creativity-based to be sustainable and innovative businesses. They need requisite holism in their conception and cybernetics in their action to succeed in this complex move. We suggest a cybernetic model aimed at meeting this need. [ABSTRACT FROM AUTHOR]

Presents information on articles and reviews published in September 2003 issue of the journal 'Cybernetics and Systems.' Theme of the articles published in the journal; Excerpts from the writings of Eugene J. Meehan, professor at University of Missouri in Saint Louis, Missouri included in the journal.

Editorial. Presents an overview of selected contributions to the workshop 'Grounding Emotions in Adaptive Systems' held at the 5th International Conference on Simulation of Adaptive Behavior in Zurich, Switzerland on August 21, 1998. Machinery for emotions; Emotions and adaptation in autonomous agents; Grounding problem of modeling emotions in adaptive systems.

Why would we want to endow artificial autonomous agents with emotions? The main answer to this question seems to rely on what has been called the functional view of emotions, arising from (analytic) studies of natural systems. In this paper, I examine to what extent this hypothesis can be applied to the (synthetic) investigation of artificial emotions and what are its implications for the design of emotional agents, the main approaches that can be appropriately used to model emotions in autonomous agents, and why situated autonomous agents provide a good framework to study the relation between emotion and adaptation. [ABSTRACT FROM AUTHOR]

Application of intelligent methods in industry become a very challenging issue nowadays and will be of extreme importance in the future. Intelligent methods include fuzzy sets, neural networks, genetics algorithms, and other techniques known as soft computing. No doubt, rough set theory can also contribute essentially to this domain. In this paper, basic ideas of rough set theory are presented and some possible intelligent industrial applications outlined. [ABSTRACT FROM AUTHOR]

State space search is the most fundamental concept in artificial intelligence AI. In this paper we examine the notion of co-state for problem solving. To understand the rationale of this extension, we describe two applications which employ the co-state concept, namely, expert system explanation and detective story analysis. We also argue how co-states can be related to the studies of constraints and diagrammatic reasoning. [ABSTRACT FROM AUTHOR]

In this paper, we propose a Neural Knowledge DNA (NK-DNA)-based framework that is capable of learning from the car’s daily operations and reusing such learned knowledge in future tasks. The NK-DNA is a novel knowledge representation and reasoning approach designed to support discovering, storing, reusing, improving, and sharing knowledge among machines and computing devices. We examine our framework for drivers’ classification based on their driving behaviors. The experimental data are collected via smartphone sensors. The initial results are presented, and the direction for our future research is defined. [ABSTRACT FROM PUBLISHER]

Even though the first publication in the area of fuzzy set theory (FST)--one of the ingredients of computational intelligence (CI)--appeared in 1965, the development of this theory for almost 20 years remained in the academic realm. Almost all basic concepts, theories, and methods were, however, developed during this period. Fuzzy control opened the gate to real applications for FST. Particularly in Japan, the applications of the fuzzy control principle in consumer goods made FST known in the public and made it commercially interesting for industry. This led to two developments: because the development of fuzzy applicational systems had to be efficient, fuzzy CASE tools and expert system shells were developed, making FST into fuzzy technology. The success in Japan could draw the attention of the media and started--first in Germany--the "fuzzy booms," which led to an unprecedented growth in publications, university teaching, and other industrial applications in many countries. Around 1993, FST, neural nets, and evolutionary computing joined forces and were soon considered to be one area called soft computing, or CI. Applications in engineering as well as in management became very numerous. One of the areas in which CI is increasingly applied is environmental engineering, planning or science. The character of CI--particularly of FST--is described and real and potential applications of CI in this area are discussed. [ABSTRACT FROM AUTHOR]

Editorial. Comments on the intelligent techniques used for product development in an industry. Application of artificial intelligence methodologies and related technologies; Fundamental ideas of rough set theory; Overview of the design, implementation and application of nueroarchitecture as neurosimulators and neuemulators.

This overview of computing for law considers the perspective of legal firms, before turning to differences of jurisdiction, to regulatory changes, to implications for staffing and access to justice. After a taxonomy of applications, a section on procedural support systems is followed with ones on discovery, on predicting the likely outcome of litigation, on argumentation, on case-based automated reasoning and abductive reasoning (dealing in a subsection with descriptive vs prescriptive software for modeling or supporting sentencing), and then to various artificial intelligence approaches to legal evidence. Before concluding, we consider the Bayesian controversy among legal evidence scholars. [ABSTRACT FROM AUTHOR]

We describe how adversarial risk analysis may be used to support the decision making of an autonomous agent that needs to interact with other agents and persons within a competitive environment. We propose several contextualizations for the problem and suggest the conceptual solution in the proposed scenarios. [ABSTRACT FROM PUBLISHER]

Many data mining problems involve an investigation of the relationships between features in heterogeneous data sets, where different learning algorithms can be more appropriate for different regions. The author proposes herein a technique of integrating global and local voting of classifiers. A comparison with other well-known combining methods on standard benchmark data sets was performed, and the accuracy of the proposed method was greater. [ABSTRACT FROM AUTHOR]

Single robot hose transport is a limit case of linked multicomponent robotic systems, where one robot moves the tip of a hose to a desired position. The interaction between the passive, flexible hose and the robot introduces highly nonlinear effects in the system's dynamics, requiring innovative control design approaches, such as reinforcement learning. This article improves previous approaches to this problem by introducing a novel reinforcement learning algorithm (TRQ-learning) and a new system state definition for the autonomous derivation of the hose–robot control algorithm. Computational experiments based on accurate geometrically exact dynamic splines hose dynamics simulations show the improvement obtained. [ABSTRACT FROM PUBLISHER]

We present and discuss the AIS Library (AIS-Lib) that extends the Jist/SWANS network simulation platform. It implements basic features of artificial immune systems (AIS). AIS are one of the most recent approaches in computational intelligence, inspired by the efficiency of the human immune system. [ABSTRACT FROM AUTHOR]

It was only in the first decade of the new century that the field of legal evidence became visible within artificial intelligence and the law (AI & Law), a thriving discipline that had developed during the previous three decades, from early concern with deontic logic to an array of both practical and theoretical approaches. In this article, we discuss a number of topics within computing for legal evidence. The average practitioner of knowledge-based systems will need no specialistic background knowledge in the legal domain, to benefit from this overview. The field is novel, yet a mass of research has already been published. It has not been previously organized in the manner presented here. [ABSTRACT FROM AUTHOR]

Artificial immune systems (AIS) are one of the most recent approaches in computational intelligence. This emerging field is sometimes referred to as immunological computation. The development of AIS is fostered by the advances made in understanding the complex mechanisms of the biological immune system. They provide efficient and robust information processing capabilities for solving complex problems. They can learn, adapt previously learned information, and perform pattern recognition/classification in a distributed way. This article discusses AIS especially in the context of misbehavior detection in wireless ad hoc (sensor) networks. [ABSTRACT FROM AUTHOR]

In this article we experiment with a 2-player strategy board game where playing models are developed using reinforcement learning and neural networks. The models are developed to speed up automatic game development based on human involvement at varying levels of sophistication and density when compared to fully autonomous playing. The experimental results suggest a clear and measurable association between the ability to win games and the ability to do that fast, while at the same time demonstrating that there is a minimum level of human involvement beyond which no learning really occurs. [ABSTRACT FROM AUTHOR]

Leadership requires making decisions and implementing the results by influencing those being lead. The Personal Access to Leadership project, PAL, is constructing tools to assist leaders in creative ways and assisting the development of leaders. The tools are knowledge-based systems employing shallow understanding of the domain. The approaches used provide guidance, but do not generate solutions. One aspect that continues under exploration in PAL is the use of stories for training and guiding leaders. In order to make such support systems work, a representation is needed to enable locating useful stories related to the task of a leader. This article defines a model for story representation for the PAL tools, called the PAL tool IdeaLab. The IdeaLab is the tool to which stories are being added, as a help system to support and extend the user's thinking. [ABSTRACT FROM AUTHOR]

This article will compare two different fuzzy-derived techniques for controlling small internal combustion engine and modeling fuel spray penetration in the cylinder of a diesel internal combustion engine. The first case study is implemented using conventional fuzzy-based paradigm, where human expertise and operator knowledge were used to select the parameters for the system. The second case study used an adaptive neuro-fuzzy inference system (ANFIS), where automatic adjustment of the system parameters is affected by a neural networks based on prior knowledge. The ANFIS model was shown to achieve an improved accuracy compared to a pure fuzzy model, based on conveniently selected parameters. Future work is concentrating on the establishment of an improved neuro-fuzzy paradigm for adaptive, fast and accurate control of small internal combustion engines. [ABSTRACT FROM AUTHOR]

A new approach, designed for clustering of arbitrary distributed patterns, is presented. This study is concerned with the use of a self-organizing neural network as a frame for data clustering. The nearest network nodes in feature space are treated as prototypes, assigned to the corresponding cluster. The rules for dead-node shifting and simple adjustment of coordinates of the active nodes are introduced. The performance of the proposed self-organizing neural network is examined on the benchmark synthetic and the real-world problem. [ABSTRACT FROM AUTHOR]

Present multimedia services are provided through a heterogeneous set of networks. Because of the heterogeneity of the networks, long-term resource availability guarantees are difficult to obtain. Consequently, even if the resource requirements throughout the service could be accurately mapped, it would not be feasible to provide overall performance guarantees. Application adaptation arises as an appropriate solution for quality-of-service assurance in such dynamic service infrastructures. Application adaptation basically involves adapting application characteristics according to the network resource availability. We formulate the problem of adaptation of multimedia applications to network infrastructure as well as to user- and application-imposed constraints and preferences. We benefit from the good approximation, identification, and control capabilities of recurrent high-order neural networks and we introduce an algorithm that guarantees the resource constraints of the network infrastructure will not be violated while maintaining the user and application requirements. [ABSTRACT FROM AUTHOR]

There are 64 possible codons that map to 20 amino acids. Each amino acid is represented by one to six codons. We were interested in using the simplest possible neural network architecture to correlate codons and amino acids. We found that the network learned and classified better if targets were represented by a more spread out format (called a training wheel). We also verified the implication of the hidden nodes, as suggested by Tolstrup et al., namely that the values of hidden nodes cluster according the Goldman, Engelman, Steitz (GES) scale of amino acid transfer free energies. [ABSTRACT FROM AUTHOR]

Rough set theory, which emerged about 20 years ago, is nowadays a rapidly developing branch of artificial intelligence and soft computing. We will use rough set theory for modeling a classification system and applying genetic operations to a population of trees, which will be induced randomly or via the C4.5 method from the decision table with different pruning-constant settings. At first glance, the methodologies we discuss, namely, rough set theory and genetic programming, have nothing in common. However, it is interesting to try to incorporate these approaches into the hybrid system. The challenge is to get as much as possible from this association. [ABSTRACT FROM AUTHOR]

Agile manufacturing can be defined as the capability of surviving in a competitive environment of continuous and unpredictable change by reacting quickly and effectively to changing markets, driven by customer-designed products and services. In such an environment, most, if not all, decisions have to be made precisely and in an intelligent manner. Intelligent decisions can only be made if we are aware of all aspects of our business and operation, which is not easily achieved. Simulation modelling can help in developing a proper level of this awareness needed in agile manufacturing environment. [ABSTRACT FROM AUTHOR]

In this article, we examine ways to organize an intelligent autonomous agent's goal system to simulate the behavior of a human worker in an enterprise modelling system called goal orientated multi-agent simulation environment (GOMASE). In GOMASE, an agent is supplied with a goal hierarchy, activity digraph, and a motivational mechanism in an attempt to simulate the goal's activation and attempted satisfaction. The attractiveness of this approach is that all the goals possessed by the agent are related via the ultimate life goals (i.e., self-preservation and species preservation). By using GOMASE to model enterprises, the enterprise modeller can gain an insight into the complex behavior of human workers and how they set, prioritize, and execute their goals. In this article, we also provide several sample runs of our system, with a brief explanation on the agent's behaviors. [ABSTRACT FROM AUTHOR]

We present a preliminary definition and theory of artificial emotion viewed as a sequential process comprising the appraisal of the agent global state, the generation of an emotion-signal, and an emotion-response. This theory distinguishes cognitive from affective appraisal on an architecture-grounded basis. Affective appraisal is performed by the affective component of the architecture; cognitive appraisal is performed by its cognitive component. A scheme for emotion classification with seven dimensions is presented. Among them, we emphasize the roles played by emotions and the way these roles are fulfilled. It is shown how emotions are generated, represented, and used in the Salt & Pepper architecture for autonomous agents (Botelho, 1997). Salt & Pepper is a specific architecture comprising an affective engine, a cognitive and behavioral engine, and an interruption manager. Most properties of the cognitive and behavioral engine rely upon a hybrid associative, schema-based long-term memory. In Salt & Pepper, emotion-signals, represented by label, object of appraisal, urgency, and valence, are generated by the affective engine through the appraisal of the agent's global state. For each emotion-signal there are several nodes stored and interconnected in long-term memory. Each of these nodes contains an emotion response that may be executed when an emotion-signal is generated. Emotion intensity relates to the activation of the node. It is shown that the Salt & Pepper architecture for autonomous agents exhibits several properties usually related to emotion: state and mood congruence, compound emotions, autonomic emotion-responses, and different emotion-responses to the same stimulus including the generation of different motives. The implementation of a concrete example is described. [ABSTRACT FROM AUTHOR]

The fact that emotions are considered to be essential to human reasoning suggests that they might play an important role in autonomous robots as well. In particular, the decision of when to interrupt ongoing behavior is often associated with emotions in natural systems. The question under examination here is whether this role of emotions can be useful for a robot which adapts to its environment. For this purpose, an emotion model was developed and integrated in a reinforcement-learning framework. Robot experiments were done to test an emotion-dependent mechanism for the automatic detection of the relevant events of a learning task against more traditional approaches. Experimental results are presented that confirm that emotions can be useful in this role, specifically by improving the efficiency of the learning algorithm. [ABSTRACT FROM AUTHOR]

Focuses on the Austrian Research Institute for Artificial Intelligence. Evolution of the company; Alliance with the Department of Medical Cybernetics and Artificial Intelligence of the University of Vienna; List of projects with industrial partners.

Marco Somalvico who introduced artificial intelligence, robotics, as well as, later on, home automation into Italian academic curricula and research, died on November 17, 2002. In 1991, the city council of Milan, Italy awarded Somalvico the Ambrogino d Oro prize as outstanding citizen. Apart from robotics, Somalvico research included automated problem solving, parallel problem solving (especially using cellular automata), automated program synthesis, natural-language processing, machine vision, multiagent systems, computer-aided design, expert systems, home and urban automation, virtual museums, man3machine interaction, and AI systems of assistance to the disabled.

Presents an approach to artificial intelligence. Explicit formulation of the theoretical foundations of situated action; Differences between artificial intelligence and connectionism; Notion of parallel distributed processing.