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New approaches are needed that could move us towards developing effective applicable intelligent systems for problem solving and decision making, One of the main efforts in intelligent systems development is focused on knowledge and information management which is regarded as the crucial issue in smart decision making support. The 14 Chapters of this book represent a sample of such effort. The overall aim of this book is to provide guidelines to develop tools for smart processing of knowledge and information. Still, the guide does not presume to give ultimate answers. Rather, it poses ideas and case studies to explore the complexities and challenges of modern knowledge management issues. It also encourages its reader to become aware of the multifaceted interdisciplinary character of such issues. The premise of this book is that its reader will leave it with a heightened ability to think - in different ways - about developing, evaluating, and supporting intelligent knowledge and information management systems in real life based environment.



Immuno-inspired Knowledge Management for Ad Hoc Wireless Networks

During the last years approaches inspired by biological immune systems showed promising results in the field of misbehavior detection and classification of data in general. In this chapter we give a comprehensive overview on the recent developments in the area of biologically inspired classification approaches of possible threats and misbehavior, especially in the area of ad hoc networks. We discuss numerous immuno related approaches, such as negative selection, B-cell cloning, Dendritic cell algorithm or Danger signals. We review present approaches and address their applicability to ad hoc networks. We further discuss challenges in translating functionality of the biological immune system to technical systems.
Martin Drozda, Sven Schaust, Helena Szczerbicka

Immune Decomposition and Decomposability Analysis of Complex Design Problems with a Graph Theoretic Complexity Measure

Large scale problems need to be decomposed for tractability purposes. The decomposition process needs to be carefully managed to minimize the interdependencies between sub-problems. A measure of partitioning quality is introduced and its application in problem classification is highlighted. The measure is complexity based (real complexity) and can be employed for both disjoint and overlap decompositions. The measure shows that decomposition increases the overall complexity of the problem, which can be taken as the measure’s viability indicator. The real complexity can also indicate the decomposability of the design problem, when the complexity of the whole after decomposition is less than the complexity sum of sub-problems. As such, real complexity can specify the necessary paradigm shift from decomposition based problem solving to evolutionary and holistic problem solving.
Mahmoud Efatmaneshnik, Carl Reidsema, Jacek Marczyk, Asghar Tabatabaei Balaei

Towards a Formal Model of Knowledge Sharing in Complex Systems

Knowledge sharing between various components of a system is a prerequisite for a successful knowledge management system. A knowledge sharing model includes providing knowledge workers with the knowledge, experiences and insights which they need to perform their tasks. We propose a multi-agent system that assists in the process of knowledge sharing between concerned knowledge worker groups. Each agent is a knowledge broker and organizer for a specialized knowledge worker group involved in the operations of a sub-system. In addition to timely access to knowledge, it should help in understanding the motivation which underlies decisions made by other groups and/or the information/knowledge bases for such decisions. Each agent is expected to learn about the activities, contexts of decisions, knowledge employed and experiences of other knowledge workers groups whose activities are considered to be relevant to the group it represents. We shall employ Partial Information State (PIS) to represent the knowledge of the agents. We shall employ a three-valued based nonmonotonic logic for reasoning about PISs. We present a multi-agent based model of argumentation and dialogue for knowledge sharing.
Nadim Obeid, Asma Moubaiddin

Influence of the Working Strategy on A-Team Performance

An A-Team is a system of autonomous agents and the common memory. Each agent possesses some problem-solving skills and the memory contains a population of problem solutions. Cyclically solutions are being sent from the common memory to agents and from agents back to the common memory. Agents cooperate through selecting and modifying these solutions according to the user-defined strategy referred to as the working strategy. The modifications can be constructive or destructive. An attempt to improve a solution can be successful or unsuccessful. Agents can work asynchronously (each at its own speed) and in parallel. The A-Team working strategy includes a set of rules for agent communication, selection of solution to be improved and management of the population of solutions which are kept in the common memory. In this paper influence of different strategies on A-Team performance is investigated. To implement various strategies the A-Team platform called JABAT has been used. Different working strategies with respect to selecting solutions to be improved by the A-Team members and replacing the solutions stored in the common memory by the improved ones are studied. To evaluate typical working strategies the computational experiment has been carried out using several benchmark data sets. The experiment shows that designing effective working strategy can considerably improve the performance of the A-Team system.
Dariusz Barbucha, Ireneusz Czarnowski, Piotr Jędrzejowicz, Ewa Ratajczak-Ropel, Iza Wierzbowska

Incremental Declarative Process Mining

Business organizations achieve their mission by performing a number of processes. These span from simple sequences of actions to complex structured sets of activities with complex interrelation among them. The field of Business Processes Management studies how to describe, analyze, preserve and improve processes. In particular the subfield of Process Mining aims at inferring a model of the processes from logs (i.e. the collected records of performed activities). Moreover, processes can change over time to reflect mutated conditions, therefore it is often necessary to update the model. We call this activity Incremental Process Mining. To solve this problem, we modify the process mining system DPML to obtain IPM (Incremental Process Miner), which employs a subset of the \(\mathcal{S}\)CIFF language to represent models and adopts techniques developed in Inductive Logic Programming to perform theory revision. The experimental results show that is more convenient to revise a theory rather than learning a new one from scratch.
Massimiliano Cattafi, Evelina Lamma, Fabrizio Riguzzi, Sergio Storari

A Survey on Recommender Systems for News Data

The advent of online newspapers broadened the diversity of available news’ sources. As the volume of news grows, so does the need for tools which act as filters, delivering only information that can be considered relevant to the reader. Recommender systems can be used in the organization of news, easing reading and navigation through newspapers. Employing the users’ history on items consumption, user profiles or other source of knowledge, these systems can personalize the user experience, reducing the information overload we currently face. This chapter presents these recommender filters, explaining their particularities and applications in the news’ domain.
Hugo L. Borges, Ana C. Lorena

Negotiation Strategies with Incomplete Information and Social and Cognitive System for Intelligent Human-Agent Interaction

Finding the adequate (win-win solutions for both parties) negotiation strategy with incomplete information for autonomous agents, even in one-toone negotiation, is a complex problem. Elsewhere, negotiation behaviors, in which the characters such as conciliatory, neutral, or aggressive define a ‘psychological’ aspect of the negotiator personality, play an important role. More, learning in negotiation is fundamental for understanding human behaviors as well as for developing new solution concepts of teaching methodologies of negotiation strategies (skills). First part of this Chapter aims to develop negotiation strategies for autonomous agents with incomplete information, where negotiation behaviors, based on time-dependent behaviors, are suggested to be used in combination (inspired from empirical human negotiation research). The suggested combination of behaviors allows agents to improve the negotiation process in terms of agent utilities, round number to reach an agreement, and percentage of agreements. Second part of this Chapter aims to develop a SOcial and COgnitive SYStem (SOCOSYS) for learning negotiation strategies from interaction (human-agent or agent-agent), where the characters conciliatory, neutral, or aggressive, are suggested to be integrated in negotiation behaviors (inspired from research works aiming to analyze human behavior and those on social negotiation psychology). The suggested strategy displays the ability to provide agents, through a basic buying strategy, with a first intelligence level, with SOCOSYS to learn from interaction (human-agent or agent-agent).
Amine Chohra, Arash Bahrammirzaee, Kurosh Madani

Intelligent Knowledge-Based Model for IT Support Organization Evolution

The goal of the paper is building the knowledge-based model for predicting the state of the IT support organization. These organizations are facing the problem of their transformation. The complexity of the processes, the difficulty adjusting the operations and limited ability to control the evolution implies the need of the solutions supporting the decision-makers to make the change happen. The solutions are based on systems gathering and processing the knowledge, built in the research units for the support of business organizations. The paper is the example of the applying this research for the banking sector. The result of the research is the intelligent, knowledge-based system used for the assessment of the organization and support the evolution of the organizations based on fuzzy modeling and mechanisms of reasoning using uncertain and incomplete knowledge and classification according to the ITIL (IT Infrastructure Library) model. The sources of the knowledge are the authors’ experiences in managing the IT support organization in the banking sector. The paper presents the results in two areas. One of them is the financial sector, for which we propose the prognostic model for the IT support organization. The second is the selection of reasoning in social systems with uncertain and incomplete knowledge. In the paper we present the mechanisms of reasoning based on the knowledge and data in the research in the evolution of the IT support organization in the financial institution. The publication contains the description of the assumptions of the experiment, the experiment itself, the analysis of its results and conclusions.
Jakub Chabik, Cezary Orłowski, Tomasz Sitek

Modeling Context for Digital Preservation

Digital preservation can be regarded as ensuring communication with the future, that means ensuring the persistence of digital resources, rendering them findable, accessible and understandable for supporting contemporary reuse as well as safeguarding the interests of future generations. The context of a digital object to be preserved over time comprises the representation of all known properties associated with it and of all operations that have been carried out on it. This implies the information needed to decode the data stream and to restore the original content, information about its creation environment, including the actors and resources involved, and information about the organizational and technical processes associated with the production, preservation, access and reuse of the digital object. In this article we propose a generic context model which provides a formal representation for capturing all these aspects, to enable retracing information paths for future reuse. Building on experiences with the preservation of digital documents in so-called memory institutions, we demonstrate the feasibility of our approach within the domain of scientific publishing.
Holger Brocks, Alfred Kranstedt, Gerald Jäschke, Matthias Hemmje

UML2SQL—A Tool for Model-Driven Development of Data Access Layer

The article is a condensed journey over UML2SQL: a tool for modeldriven development of data access layer. UML2SQL includes an object query language and allows for behavior modeling based on UML activity diagrams, effectively linking structural and behavioral aspects of the system development. From the general idea of UML2SQL and its origins, we go through the details of its architecture and beyond the processes and schemes which make UML2SQL a distinct tool in the data access domain. Finally, an example of developing an application using UML2SQL is given as an illustration of its practical usage.
Leszek Siwik, Krzysztof Lewandowski, Adam Woś, Rafał Dreżewski, Marek Kisiel-Dorohinicki

Fuzzy Motivations in Behavior Based Agents

In this chapter we describe a fuzzy logic based approach for providing biologically based motivations to be used by agents in evolutionary behavior learning. In this approach, fuzzy logic provides a fitness measure used in the generation of agents with complex behaviors which respond to user expectations of previously specified motivations. Our approach is implemented in behavior based navigation, route planning and action sequence based environment recognition tasks in a Khepera mobile robot simulator. Our fuzzy logic based motivation technique is shown as a simple and powerful method for agents to acquire a diverse set of fit behaviors as well as providing an intuitive user interface framework.
Tomás V. Arredondo

Designing Optimal Operational-Point Trajectories Using an Intelligent Sub-strategy Agent-Based Approach

This paper presents a method intended for designing optimal and safe control for nonlinear dynamical processes. The sought control signal results from elementary control strategies induced by different agents implementing their (partial) task of minimizing a common control cost measure (index). The issue of designing optimal control is therefore treated as a decision process, where the decisions are made in particular regions of the state space of the dynamical process under consideration. The regions thus constitute local decision spaces being searched by a group of agents in a multistage searching procedure. At each stage, every agent can increment its cost index only by a limited value. This guarantees that at the end of each stage all the agents represent control strategies which are cost equivalent (approximately). The algorithm starts off by generating an initial population of agents (each for one of the previously defined elementary control strategies). Each of these agents realizes a different kind of possible elementary control strategies, which determine predefined agent behaviors. When an agent reaches one of the decision regions, it generates a new/local population of the seeking/hunting agents (they are, again, of different kinds of the elementary control strategies). After getting explored, such a decision region turns to a forbidden zone for all agents but those belonging to the newly created population. In such a way the successive populations of the agents allow to complete the path to a prearranged destination point in a competitive way. The first agent which reaches the destination area in the state space determines an optimal solution in the sense of the above assumptions.
Zdzislaw Kowalczuk, Krzysztof E. Olinski

An Ontology-Based System for Knowledge Management and Learning in Neuropediatric Physiotherapy

This chapter first presents an extensive review of the current state of art in knowledge management and ontologies. Next, we propose a methodology for modeling and building an ontology-based system for knowledge management in the domain of Neuropediatric Physiotherapy and its application to supporting learning. This area of Physiotherapy includes diagnosis, treatment and evaluation of patients with neurological injuries. The domain knowledge in Physiotherapy is, by nature, complex, ambiguous and non-standardized. In this work knowledge was elicited from domain experts and complemented with information from reference textbooks. The acquired knowledge was represented as an ontology. The formal procedures allowed the development of a knowledge-base for further use in an educational tool. The completeness and consistency of formal model was verified. Overall, the main contribution of the work are a domain ontology based on consensus vocabulary for an important area of health sciences, and the possibility of using it as a tool for supporting the learning of undergraduate students. In particular, the application of the ontology for learning in Physiotherapy is of great importance, since it includes multimedia resources as well as active learning concepts, together with traditional instructional methods.
Luciana V. Castilho, Heitor S. Lopes

Mining Causal Relationships in Multidimensional Time Series

Time series are ubiquitous in all domains of human endeavor. They are generated, stored, and manipulated during any kind of activity. The goal of this chapter is to introduce a novel approach to mine multidimensional time-series data for causal relationships. The main feature of the proposed system is supporting discovery of causal relations based on automatically discovered recurring patterns in the input time series. This is achieved by integrating a variety of data mining techniques.
The main insight of the proposed system is that causal relations can be found more easily and robustly by analyzing meaningful events in the time series rather than by analyzing the time series numerical values directly. The RSST (Robust Singular Spectrum Transform) algorithm is used to find interesting points in every time series that is further analyzed by a constrained motif discovery algorithm (if needed) to learn basic events of the time series. The Granger-causality test is extended and applied to the multidimensional time-series describing the occurrences of these basic events rather than to the raw time-series data.
The combined algorithm is evaluated using both synthetic and real world data. The real world application is to mine records of activities during a human-robot interaction experiment in which a human subject is guiding a robot to navigate using free hand gesture. The results show that the combined system can provide causality graphs representing the underlying relations between the human’s actions and robot behavior that cannot be recovered using standard causal graph learning procedures.
Yasser Mohammad, Toyoaki Nishida


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