Fundamenta Informaticae - Volume 30, issue 3-4

Authors: Michalewicz, Maciej | Ras, Zbigniew W.

Article Type: Other

DOI: 10.3233/FI-1997-303413

Citation: Fundamenta Informaticae, vol. 30, no. 3-4, pp. i-ii, 1997

Authors: Clark, Peter | Feng, Cao | Matwin, Stan | Fung, Ko

Article Type: Research Article

Abstract: Evidence for image classification can be considered to come from two sources: traditional statistical information derived algorithmically from image data, and model-based evidence arising from previous expertise and experience in a given application domain. This paper presents a study of classification techniques based on both these sources (traditional algorithmic and model-based), and illustrates how they can be combined. A prototype image classification system, called Cabaress, has been constructed which implements these methods. We evaluate Cabaress as applied to the problem of identifying crops in agricultural fields, based on classifying image segments extracted from radar image data. Our results demonstrate this …mixed-method approach can achieve improved classificational accuracy. Show more

DOI: 10.3233/FI-1997-303401

Citation: Fundamenta Informaticae, vol. 30, no. 3-4, pp. 227-240, 1997

Authors: Druzdzel, Marek J.

Article Type: Research Article

Abstract: Although probabilistic knowledge representations and probabilistic reasoning have by now secured their position in artificial intelligence, it is not uncommon to encounter misunderstanding of their foundations and lack of appreciation for their strengths. This paper describes five properties of probabilistic knowledge representations that are particularly useful in intelligent systems research. (1) Directed probabilistic graphs capture essential qualitative properties of a domain, along with its causal structure. (2) Concepts such as relevance and conflicting evidence have a natural, formally sound meaning in probabilistic models. (3) Probabilistic schemes support sound reasoning at a variety of levels ranging from purely quantitative to purely …qualitative levels. (4) The role of probability theory in reasoning under uncertainty can be compared to the role of first order logic in reasoning under certainty. Probabilistic knowledge representations provide insight into the foundations of logic-based schemes, showing their difficulties in highly uncertain domains. Finally, (5) probabilistic knowledge representations support automatic generation of understandable explanations of inference for the sake of user interfaces to intelligent systems. Show more

DOI: 10.3233/FI-1997-303402

Citation: Fundamenta Informaticae, vol. 30, no. 3-4, pp. 241-254, 1997

Authors: Grzymala-Busse, Jerzy W.

Article Type: Research Article

Abstract: Very frequently machine learning from real-life data is affected by uncertainty. There are three main reasons for imperfection in data: incompleteness, imprecision (also called vagueness), and errors. In this paper the main emphasis is on classification of unseen examples using a rule set induced from imperfect data. The classification strategy of the machine learning system LERS is described in detail. Results of experiments with medical data sets are also reported.

DOI: 10.3233/FI-1997-303403

Citation: Fundamenta Informaticae, vol. 30, no. 3-4, pp. 255-267, 1997

Authors: Kersten, Gregory E. | Szpakowicz, Stan

Article Type: Research Article

Abstract: It is typical for an autonomous agent to make decisions in the presence of other agents who respond, react to his action's and undertake actions independently of the agent's decisions. Also, the decision environment is often dynamic and certain changes cannot be forseen. This requires the agent to have an ability to recognize and interpret the situation he faces. The agent needs additional abilities to evaluate the implemented decisions, assess the actions of the others and determine the representation of his current decision problem. This paper gives a formal account of an agent who has specific cognitive and representational abilities …required to make sequential decisions in co-operative settings. The restructurable modelling methodology, presented here, has been implemented in the Negoplan system and applied to the simulation, analysis and support of negotiations, patient diagnosis and treatment, investment scenario development, planning and distributed decision-making. Show more

DOI: 10.3233/FI-1997-303404

Citation: Fundamenta Informaticae, vol. 30, no. 3-4, pp. 269-281, 1997

Authors: Michalski, Ryszard S.

Article Type: Research Article

Abstract: An enormous proliferation of computer technology in modern societies has produced a severe information overload. The navigation through the masses of available information in order to derive desired knowledge is becoming increasingly difficult. This creates a demand for intelligent systems capable of assisting data analysts in extracting goal-oriented knowledge from large volumes of data. This paper presents a multistrategy methodology and a system, INLEN, for knowledge discovery in large relational databases. The system integrates data base, knowledge base and machine learning technologies. It offers a data analyst an integrated interface and a wide range of knowledge generation operators, as described …in the Inferential Theory of Learning. Presented ideas are illustrated by results from experiments with INLEN. Show more

DOI: 10.3233/FI-1997-303405

Citation: Fundamenta Informaticae, vol. 30, no. 3-4, pp. 283-297, 1997

Authors: Mrózek, Adam | Płonka, Leszek

Article Type: Research Article

Abstract: Alternative approaches to data processing, e.g., fuzzy set theory, rough set theory and neural networks, known collectively as ‘soft computing,’ have drawn significant interest from the scientific community. They have already found numerous applications to real-world problems and will certainly become even more important in the future. This paper concentrates on two important classes of soft computing methods, i.e., fuzzy sets and rough sets. In particular, knowledge acquisition and representation in fuzzy and rough controllers is discussed and compared. The paper is concluded with an illustrative example, showing the application of fuzzy and rough set theory to the control of …the ‘inverted pendulum.’ Show more

DOI: 10.3233/FI-1997-303406

Citation: Fundamenta Informaticae, vol. 30, no. 3-4, pp. 299-311, 1997

Authors: Ras, Zbigniew W. | Joshi, Sucheta

Article Type: Research Article

Abstract: A Distributed Knowledge-Based System (DKBS) is a collection of autonomous knowledge-based systems called agents which are capable of interacting with each other. A query can be submitted to one agent or a group of agents. An agent when contacted by the user acts as a master agent. If he is unable to answer the query, he looks for help from other agents which act as his slaves. In this paper, an agent is represented by an information system (either complete or incomplete), a collection of rules called a knowledge base and the Query Approximate Answering System (QAAS). Rules are interpreted …as descriptions of some attribute values in terms of other attribute values. These descriptions are usually not precise and they only provide a lower approximation of attribute values. We say that an attribute value is reachable by an agent if either it belongs to the domain of one of the attributes in his information system or it is a decision part of one of the rules in his knowledge base. In the second case, we assume that all attribute values from the classification part of a rule have to be reachable. When rules are discovered by one site of DKBS which currently acts as a slave, they are sent to the master agent of that slave. The QAAS of the master agent will use these rules to answer a query submitted by the user and next it will store these rules in the agent's knowledge base. So, the set of reachable attribute values at any site of DKBS is constantly changing. Knowledge bases built that way might easily become inconsistent because rules they contain are created independently at different sites of DKBS. The problem of repairing inconsistent rules was investigated in [19]. In this paper, we propose a strategy for discovering rules in incomplete information systems and give a formal system for handling queries in DKBS where each site contains either an incomplete or a complete information system. Show more

Keywords: incomplete information system, query answering, rough sets, multi-agent system, knowledge discovery

DOI: 10.3233/FI-1997-303407

Citation: Fundamenta Informaticae, vol. 30, no. 3-4, pp. 313-324, 1997

Authors: Rusinkiewicz, Marek | Bregolin, Mauro

Article Type: Research Article

Abstract: The basic transaction model has evolved over time to incorporate more complex transaction structures and to selectively modify the atomicity and isolation properties. In this paper we discuss the application of transaction concepts to activities that involve coordinated execution of multiple tasks (possibly of different types) over different processing entities. Such applications are referred to as transactional workflows. We discuss some of the issues involved in specification and execution of such workflows.

DOI: 10.3233/FI-1997-303408

Citation: Fundamenta Informaticae, vol. 30, no. 3-4, pp. 325-344, 1997

Authors: Skowron, Andrzej | Polkowski, Lech

Article Type: Research Article

Abstract: In this paper we present some strategies for synthesis of decision algorithms studied by us. These strategies are used by systems of communicating agents and lead from the original (input) data table to a decision algorithm. The agents are working with parts of data and they compete for the decision algorithm with the best quality of object classification. We give examples of techniques for searching for new features and we discuss some adaptive strategies based on the rough set approach for the construction of a decision algorithm from a data table. We also discuss a strategy of clustering by tolerance.

DOI: 10.3233/FI-1997-303409

Citation: Fundamenta Informaticae, vol. 30, no. 3-4, pp. 345-358, 1997