An Approach to Embedding of Agents into Knowledge Representation and Processing System

The concept of stratified graph introduces some method of knowledge representation (see [T¸ and ˘ areanu, N., ˘ Knowledge representation by labeled stratified graphs, Proc. 8th World Multi-Conference on Systemics, Cybernetics and Informatics, 5 (2004), 345–350; T¸ and ˘ areanu, N., ˘ Proving the Existence of Labelled Stratified Graphs, An. Univ. Craiova Ser. Mat. Inform., 27 (2000), 81–92]) The inference process developed for this method uses the paths of the stratified graphs, an order between the elementary arcs of a path and some results of universal algebras. The order is defined by considering a structured path instead of a regular path. In this paper we define the concept of system of knowledge representation as a tuple of the following components: a stratified graph G, a partial algebra Y of real objects, an embedding mapping (an injective mapping that embeds the nodes of G into objects of Y ) and a set of algorithms such that each of them can combine two objects of Y to get some other object of Y . We define also the concept of inference process performed by a system of knowledge processing in which the interpretation of the symbolic elements is defined by means of natural language constructions. In this manner we obtained a mechanism for texts generation in a natural language (for this approach, Romanian).

In connection with the transition to a knowledge-based economy, at a time when a key factor in the development of society is the accumulated human knowledge and skills, as well as the availability of a wide range of users, intelligent systems are becoming very popular. Accordingly, the demand of the ergonomic and effective means of designing this class system is growing as well. The most time-consuming and most important stage of intelligent system development is the formation of the system knowledge base which ultimately determines the efficiency and quality of the entire intelligent system. Knowledge representation and processing models and methods as well as the intelligent system development techniques operating on the basis of these methods and models have a crucial role in relation to this. The article explores the different aspects of intelligent collaborative educational systems, describes the overall structure of an intelligent collaborative educational system and reflects the different steps of development the system.

Although current efforts have indicated the need for design verification and evaluation systems, little has been done to develop knowledge representation systems for implementing this class of expert systems. In this paper, we describe CANDLE, a knowledge representation system built to solve design verification and evaluation problems. We define the class of design verification and evaluation systems and the functionality required of knowledge representation systems used to develop these systems. We present an example design verification and evaluation problem, illustrating how this problem imposes these functionality requirements on the knowledge representation system used. In the remainder of the paper we discuss CANDLE, a knowledge representation system created to develop design verification and evaluation systems. We show that CANDLE meets the knowledge representation requirements for this class of systems. We also present some of CANDLE'S functionality that exceeds the defined knowledge representation requirements and provides a knowledge engineer with the capability to expand the functionality of a design verification and evaluation system.

A set of reusable ontologies has been developed as separate components within a knowledge representation (KR) system, to generically model a reef system. The ontology design, ranging from light to heavyweight, aims to leverage from the scalable characteristics of semantic technologies to allow for flexibility when posing domain and locality-specific hypotheses, such as predicting coral bleaching. The Semantic Reef Project is an eco-informatics application designed to test ecological hypotheses to derive information about environmental systems. The intention is to develop an automated data processing, problem-solving, and knowledge discovery system that will assist in developing our understanding and management of coral reef ecosystems. Remote environmental monitoring (including sensor networks) is being widely developed and used for collecting real-time data across widely distributed locations. As the volume of raw data increases, it is envisaged that bottlenecks will develop in the data analysis phases because current data processing procedures still involve manual manipulation and will soon become unfeasible to manage. Ontologies provide a new approach and methodology for modulating this data overflow while also improving our ability to extract knowledge from the data collected.

: This paper introduces the principal concepts in the organization and operation of the logic based knowledge processing system called CK-LOG (A Calculus for Knowledge in LOGic) CK-LOG uses the frame based system MDS (the Meta Description System) for knowledge representation and for modelling world states. It uses an inference engine based on Natural Deduction for stating and solving problems. As a knowledge processing system CK-LOG has several capabilities which are new to the technology of knowledge representation systems. CK-LOG has special facilities to represent and reason about actions and their time dependencies. Actions that occur in a world state may create or destroy objects in the world or modify their properties or prevent or support other actions. The effects of actions are described in CK-LOG using modal operators like CREATE, DESTROY, PREVENT, SUPPORT, KEEP, etc. These operator expressions are also used to represent and reason about possible worlds that the actions might lead to. Most significantly, CK-LOG is a logic based knowledge processing system, just as PROLOG is a logic based programming system. CK-LOG uses a three valued logical system with truth values T (true), ?. (unknown) and F (false) to build partial models of world states, and the two valued logical system of T and F in its theorem proving system. The use of the three valued logical system in its models of world states enables CK-LOG to do problem solving in the context of incomplete information about world states.

: This report introduces the principal concepts in the organization and operation of the logic-based knowledge processing system, called CK-LOG (A Calculus for Knowledge in LOGic). CK-LOG uses the frame-based system MDS (the Meta Description System) for knowledge representation and for modelling world states. It uses an inference engine based on natural Deduction for stating and solving problems. The operation of CK-LOG is discussed here in the context fo OPPLAN-CONSULTANT, an expert consultation system for Naval Operational Planning that is now being designed using the CK-LOG formalism. CK-LOG itself is currently under implementation. This report establishes the descriptive adequacy of CK-LOG to state and represent military planning knowledge needed for operational planning, as well as its problem solving adequacy to use this knowledge automatically to help military commanders design operational plans to achieve given military objectives. The organization of the planning knowledge and the planning processes in OPPLAN-CONSULTANT are directly applicable to the Battle Management Program of the Strategic Computing Initiative. As a knowledge processing system. CK-LOG has several capabilities which are new to the technology of knowledge representation systems: CK-LOG has special facilities to represent and reason about actions and their time dependencies. The theorem-proving system of CK-LOG uses a variant of the calculus of sequents first proposed by Kanger (which itself is a variant of Gentzen's system). The various features of CK-LOG are described in this report.

Retrieving and composing individuals' capabilities are the matter of several research fields, like competence-based management, human resources management, enterprise knowledge management systems, knowledge representation systems, etc. This talk focuses on capability representation, discovery and composition in heterogeneous, and possibly distributed, knowledge representation environments. A driving motivation of this work is to provide a contribution to the satisfaction of the need for retrieving individuals who may carry out actions, as well as the need for retrieving individuals who, when putting their competences together, may carry out actions. We outline some formal capability description languages, and we propose approaches and algorithms for capability management, discovery and composition. In addition, we outline how our proposals are implemented in mediator-based prototype systems under a service oriented architecture and a Peer-to-Peer architecture.

Interdisciplinary applications exposed new interesting, but difficult problems in the field of measurement. Such applications put emphasis on the automation of the measurement process on the whole. The analysis of how the experiments are planned, controlled, evaluated, etc. Brought into focus the question of how well the expert knowledge related to the measurement can be represented and utilised in the development of autonomous intelligent measurement systems. Methods developed in the field of Artificial Intelligence (AI) lend themselves naturally for such applications. The present paper proposes so called constraints to be used as the knowledge representation tool in modelling measurements. When a deeper model of the phenomena underlying the measurement processes is needed, applying constraints as knowledge representation is potentially fruitful, considering how well they express the dependencies between different subsystems and state variables of the physical systems. Authors review measurement technical problems suited for the constraint based knowledge representation, analyse the requirements of the actual constraint satisfaction problems and evaluate the known methodology from that perspective. >

The aim of the study is to develop a model of consistent knowledge about the decision-making process in intelligent systems, which would consider their logical consistency and provide an opportunity to explain the process and outcome of such systems. The object of study is the process of constructing explanations in intelligent systems. Research objectives: development of a generalized approach to the harmonization of knowledge to build explanations in intelligent information systems; development of a model for the presentation of consistent knowledge, considering their logical consistency. The following results were obtained. A generalized approach to the harmonization of knowledge to build explanations in intelligent information systems has been developed. The approach provides the possibility of forming a set of agreed explanations that reflect different aspects of the decision-making process and the result obtained by the intelligent system, which simplifies the application of these results in the subject area of the user. The model of representation of the coordinated knowledge considering their logical consistency is offered. The model makes it possible to check the consistency of knowledge directly in the formation of explanations, which creates conditions for the formation of explanations on the principle of the black box and allows to supplement functioning systems with explanations, as well as to adapt explanations when changing their functionality.

The paper considers the trends in the development of artificial intelligence technologies in BSUIR for the last five years, lists the main results obtained during this period both in the field of development of artificial intelligence technologies themselves, and in the field of education in artificial intelligence and realization of interaction between teams of specialists working in this field. The necessity of transition to new-generation intelligent computer systems with a high level of interoperability and creation of an appropriate complex technology for their development, maintenance and operation is substantiated. The problems hindering the active development and implementation of new generation intelligent computer systems are considered. The concept of semantic space as a basis for representation and integration of knowledge in intelligent computer systems of new generation is considered. The principles of implementation of hardware platform for interpretation of information processes in the semantic space – associative semantic computer – are considered.

Knowledge Organization in editorial policies for titles, abstracts and keywords in JCR-indexed journals: an exploratory study in the areas of Information and Communication Sciences

Introduction: We discuss the modern ways of developing intelligent problem solvers, focusing on their shortcomings in terms of the efficiency of their application for planning purposeful behavior of autonomous mobile intelligent systems in a priori undescribed conditions of a problem environment. Purpose: Developing a model of knowledge representation and processing which would provide the ways to organize purposeful activity of autonomous intelligent mobile systems in uncertain environment. Methods: Synthesis of frame-like behavior scenarios in the form of polyvariable conditionally dependent predicates whose structure includes complex variables as well as related variables of types “object”, “event” and “relationship”; synthesis of heuristic rules for knowledge representation in the process of purposeful behavior planning. In order to represent complex variables in polyvariable conditionally dependent predicates, fuzzy semantic networks are used which can represent knowledge of variously purposed intelligent systems without regard to particular knowledge domains, being adaptable to a priori undescribed operational conditions. Results: We have proposed a structure of various polyvariable conditionally dependent predicates. On their base, an autonomous intelligent mobile system can organize various activities in a priori undescribed and unstable problem environments. Specially developed knowledge processing tools allow such a system to automatically plan its purposeful behavior in a space of subtasks during the fulfilment of tasks formulated for it. Practical relevance: The obtained results can be efficiently used in building intelligent problem solvers for autonomous intelligent mobile systems of various purpose, capable of performing complex tasks in a priori undescribed operational conditions.

Knowledge processing is an essential technique for enabling autonomous robots to do the right thing to the right object in the right way. Using knowledge processing the robots can achieve more flexible and general behavior and better performance. While knowledge representation and reasoning has been a well-established research field in artificial intelligence for several decades, little work has been done to design and realize knowledge processing mechanisms for the use in the context of robotic control. In this paper, we report on KNOWROB, a knowledge processing system particularly designed for autonomous personal robots. KNOWROB is a first-order knowledge representation based on description logics that provides specific mechanisms and tools for action-centered representation, for the automated acquisition of grounded concepts through observation and experience, for reasoning about and managing uncertainty, and for fast inference - knowledge processing features that are particularly necessary for autonomous robot control.

Resolution-based Knowledge Representation and Reasoning (KRR) systems, such as Flora-2, Silk or Ergo, can scale to tens or hundreds of millions of facts, while supporting reasoning that includes Hilog, inheritance, defeasibility theories, and equality theories. These systems handle the termination and complexity issues that arise from the use of these features by a heavy use of tabled resolution. In fact, such systems table by default all rules defined by users, unless they are simple facts.Performing dynamic updates within such systems is nearly impossible unless the tables themselves can be made to react to changes. Incremental tabling as first implemented in XSB (Saha 2006) partially addressed this problem, but the implementation was limited in scope and not always easy to use. In this paper, we introducetransparent incremental tablingwhich at the semantic level supports updates in the 3-valued well-founded semantics, while guaranteeing full consistency of all tabled queries. Transparent incremental tabling also has significant performance improvements over previous implementations, including lazy recomputation, and control over the dependency structures used to determine how tables are updated.

In order to represent the design knowledge of complex product, logical descriptions of different knowledge are conducted by character analysis for the generalized design knowledge. Then a knowledge representation system is proposed in this chapter utilizing the three-layer structure of “design object ontology—design process ontology—data resource” (OPR). And the semantic description frame is defined based on the suggested upper merged ontology (SUMO) to implement the ontology collaborative construction using the ontology web language (OWL). Based on the constructed ontology, the storage structure for generalized knowledge is determined to actualize the generalized knowledge representation. Finally, the feasibility and effectiveness of the proposed technique in this chapter is demonstrated by the design knowledge representation for the self-propelled gun.