Hybrid Knowledge Representation Research Articles

A Review of Symbolic, Subsymbolic and Hybrid Methods for Sequential Decision Making

In the field of Sequential Decision Making (SDM), two paradigms have historically vied for supremacy: Automated Planning (AP) and Reinforcement Learning (RL). In the spirit of reconciliation, this paper reviews AP, RL and hybrid methods (e.g., novel learn to plan techniques) for solving Sequential Decision Processes (SDPs), focusing on their knowledge representation: symbolic, subsymbolic or a combination. Additionally, it also covers methods for learning the SDP structure. Finally, we compare the advantages and drawbacks of the existing methods and conclude that neurosymbolic AI poses a promising approach for SDM, since it combines AP and RL with a hybrid knowledge representation.

Managing Machine Learning Workflow Components

Machine Learning Workflows (MLWfs) have become an essential and disruptive approach in problem-solving over several industries. However, the development process of MLWfs may be complex, time-consuming, and error-prone. To handle this problem, we introduce machine learning workflow management (MLWfM) as a technique to aid the development and reuse of MLWfs and their components through three aspects: representation, execution, and creation. We introduce our approach to structure MLWfs’ components and metadata in order to aid component retrieval and reuse of new MLWfs. We also consider the execution of these components within a tool. A hybrid knowledge representation, called Hyperknowledge, frames our methodology, supporting the three MLWfM’s aspects. To validate our approach, we show a practical use case in the Oil & Gas industry. In addition, to evaluate the feasibility of the proposed technique, we create a dataset of MLWfs executions and discuss the MLWfM’s performance in loading and querying this dataset.

A Knowledge-Based System for Disaster Emergency Relief

Natural disasters have had great impact on human beings. Emergency relief is becoming more important with the increase of the frequency and scale of humanitarian emergencies resulting from more natural disasters because of climate change. In this paper, an interesting hybrid knowledge representation method during the development of a KBS for design of emergency relief structures is presented. It encapsulates ill-structured, semi-structured and structured knowledge that is gathered from literature, human expert and even knowledge gleaned during the system development. All routine as well as cumbrous activities in the emergency relief cycle are covered. The system can provide the user with advice on preliminary plan evaluation, plan optimization, plan evaluation, plan summary and miscellaneous. It would be beneficial to the field of disaster emergency relief decision by focusing on the acquisition and organization of expert knowledge through the development of knowledge-based system.

ПРОЕКТИРОВАНИЕ ЭКСПЕРТНЫХ СИСТЕМ НА ОСНОВЕ ВЫСОКОУРОВНЕВЫХ ЗНАНИЕОРИЕНТИРОВАННЫХ МОДЕЛЕЙ

The main points related to the design of the integrated decision supported expert systems are analyzed in the paper. The perspectives of hybrid knowledge representation model are considered. In order to represent the domain knowledge it is proposed to use a high level knowledge oriented model that makes it possible to describe the processes of analysis, mining, and processing of domain knowledge in a form of interaction of some typical predefined functional blocks. The main advantages of the developed functional knowledge-oriented model are: modularity (representation of monitoring, diagnostics and control processes of complex technological systems and objects in a form of separate knowledge-oriented components interaction); universality of the typical functional blocks library (solution of typical tasks, arising in the process of technological object control); adaptability (easy adaptation to a specific domain); openness (gives the user a possibility to set the custom behavior); activity (interaction of typical functional blocks with each other that makes it possible to automate the process of knowledge acquisition and processing and also interaction of functional blocks with a hybrid production-frame model that makes possible to increase the efficiency of knowledge procession during the decision making process). Every typical intelligent element is a functional block with a set of inputs {IN} and outputs {OUT}. The behavior of such block is defined by its purpose. The whole set of typical intelligent blocks that is used in construction of functional knowledge-oriented model according to the block purpose can be divided into several different classes. On the basis of the selected representation model the processes of knowledge formalization are described. The main advantages of the selected approach to formalize the domain knowledge are stated. On the basis of the represented instrumental tool structure the computer the system has been made. The main stages of expert system creation and their key features are considered. The editor of functional knowledge-oriented model has been presented. The basic functions of the editor are model visualization and debugging. Instrumental tool make possible to build control decision expert systems in different domains. The example of expert system in domain of electrochemical protection of pipelines from corrosion has been considered. The basic directions of possible updating of mathematical model and instrumental tools are described in conclusion

Natural language processing for hybrid knowledge representation

Natural language processing for hybrid knowledge representation

Natural language processing for hybrid knowledge representation

The excessive amount of knowledge increased the demand of tools for organising, processing and extracting the knowledge. To organise different types of the knowledge and to infer the knowledge intelligently is the biggest task in knowledge engineering (KE), natural language processing (NLP), information retrieval (IR) and knowledge management. The key factor for the development of this society/nation is the interactive access to information in current era. The source of information available in today's life is benefiting the people those who are familiar with the English language, but the biggest question is what about the generation who are not familiar with English not only in India but aboard too. One solution to this problem is graphical visualisation, easy and fast access to the information available through any source. This paper presents the system that provides the user-friendly interface for all the users (very good English, good English, not too good in English) for knowledge gathering, discovery and retrieval.

A Hybrid Knowledge-Base System Approach for Disaster Emergency and Relief Decision

Hybrid knowledge representation schemes, including production rules, object-oriented programming, and procedural methods, are employed to express expert heuristics and standard emergency knowledge during the development of the knowledge-based system (KBS) for emergency decision for disaster reduction. This approach renders it possible to take advantages of the characteristics of each method. The system can provide the user with advice on Preliminary plan evaluation, Plan optimization, Plan evaluation ,Plan summary and Miscellaneous.

Research and Development of Intelligent Design System for Machine Tool Fixture Based on KBE

In this paper, KBE and some key technologies, such as knowledge representation, knowledge reasoning and knowledge base design, are studied for solving the exiting design problems of modular machine tool fixtures. The methods of hybrid knowledge representation based on ontology and rule-based and case-based hybrid reasoning are introduced to intelligent design of modular machine tool fixtures. By researching the design flow of modular machine tool fixtures, a modular machine tool fixtures intelligent design system (MMTFIDS) is developed based on secondary development technology of UG/NX in the VS.NET integrated development environment. The system realizes the reuse of design knowledge and rapid design for modular machine tool fixture.

Learning of shared attention in sociable robotics

Sociable robots are embodied agents that are part of a heterogeneous society of robots and humans. They should be able to recognize human beings and each other, and to engage in social interactions. The use of a robotic architecture may strongly reduce the time and effort required to construct a sociable robot. Such architecture must have structures and mechanisms to allow social interaction, behavior control and learning from environment. Learning processes described on Science of Behavior Analysis may lead to the development of promising methods and structures for constructing robots able to behave socially and learn through interactions from the environment by a process of contingency learning. In this paper, we present a robotic architecture inspired from Behavior Analysis. Methods and structures of the proposed architecture, including a hybrid knowledge representation, are presented and discussed. The architecture has been evaluated in the context of a nontrivial real problem: the learning of the shared attention, employing an interactive robotic head. The learning capabilities of this architecture have been analyzed by observing the robot interacting with the human and the environment. The obtained results show that the robotic architecture is able to produce appropriate behavior and to learn from social interaction.

Building Rules on Top of Ontologies for the Semantic Web with Inductive Logic Programming

AbstractBuilding rules on top of ontologies is the ultimate goal of the logical layer of the Semantic Web. To this aim, an ad-hoc markup language for this layer is currently under discussion. It is intended to follow the tradition of hybrid knowledge representation and reasoning systems, such as$\mathcal{AL}$-log that integrates the description logic$\mathcal{ALC}$and the function-free Horn clausal languageDatalog. In this paper, we consider the problem of automating the acquisition of these rules for the Semantic Web. We propose a general framework for rule induction that adopts the methodological apparatus of Inductive Logic Programming and relies on the expressive and deductive power of$\mathcal{AL}$-log. The framework is valid whatever the scope of induction (description versus prediction) is. Yet, for illustrative purposes, we also discuss an instantiation of the framework which aims at description and turns out to be useful in Ontology Refinement.

Design expert system tool with hybrid knowledge representation and reasoning

According to the characteristics and requirements for design expert systems, this paper describes a Design Expert System Tool (DEST) with hybrid knowledge representation and reasoning. The main goal is to provide an effective and convenient development environment of expert system for design domains. It adopts the technique of object oriented knowledge representation, which integrates frame, rule and procedure. For each knowledge representation, different inference engines are designed. Inductive reasoning and Case-Based Reasoning (CBR) are combined to form a hybrid inference engine. For improving the quality of case retrieval, it presents a new method of attribute similarity calculation and weight assigning. Moreover, it provides a visualisation knowledge management platform for expert and designer to build and apply expert systems.

Implementing tutoring strategies into a patient simulator for clinical reasoning learning

This paper describes an approach for developing intelligent tutoring systems (ITS) for teaching clinical reasoning. Our approach to ITS for clinical reasoning uses a novel hybrid knowledge representation for the pedagogic model, combining finite state machines to model different phases in the diagnostic process, production rules to model triggering conditions for feedback in different phases, temporal logic to express triggering conditions based upon past states of the student's problem solving trace, and finite state machines to model feedback dialogues between the student and TeachMed. The expert model is represented by an influence diagram capturing the relationship between evidence and hypotheses related to a clinical case. This approach is implemented into TeachMed, a patient simulator we are developing to support clinical reasoning learning for a problem-based learning medical curriculum at our institution; we demonstrate some scenarios of tutoring feedback generated using this approach. Each of the knowledge representation formalisms that we use has already been proven successful in different applications of artificial intelligence and software engineering, but their integration into a coherent pedagogic model as we propose is unique. The examples we discuss illustrate the effectiveness of this approach, making it promising for the development of complex ITS, not only for clinical reasoning learning, but potentially for other domains as well.

An expert system on site selection of sanitary landfill

It is desirable to encapsulate heuristic and empirical knowledge including hydrological and bio-geochemical considerations into the selection process of a potential landfill site. This paper delineates a prototype expert system, which has been developed using a commercially available microcomputer-based expert system shell VISUAL RULE STUDIO, for the selection of a landfill site. VISUAL RULE STUDIO acts as an ActiveX Designer under the Microsoft Visual Basic programming environment, with the hybrid knowledge representation approach under object-oriented design environment. The blackboard architecture, which is able to group all the knowledge together into an integrated system effectively, is adopted. The evaluation is based on the hazardous waste site ranking system recommended by the US Environmental Protection Agency, adapted to Hong Kong conditions. Increase in efficiency, improvement, consistency of results and automated record keeping are among the advantages of such a system.

Prototype expert system for site selection of a sanitary landfill

It is desirable to incorporate heuristic and empirical knowledge including hydrological and bio-geochemical considerations into the selection process of a potential landfill site. In this article, a prototype expert system for the selection of a landfill site, with hybrid knowledge representation approach under object-oriented design environment in a blackboard architecture, is described. It incorporates an artificial neural network for training of partial hazardous scores and a fuzzy rule base for the representation of heuristic knowledge. The evaluation is based on the hazardous waste site ranking system recommended by the US Environmental Protection Agency, adapted to Hong Kong conditions by incorporating the stipulation of some local regulations. It is shown to be a useful aid in assisting novice engineers in the selection process of a potential landfill site during preliminary investigation.

An expert system for mix design of high performance concrete

This paper describes a prototype expert system called HPCMIX that provides proportion of trial mix of High Performance Concrete (HPC) and recommendations on mix adjustment. The knowledge was acquired from various textual sources and human experts. The system was developed using hybrid knowledge representation technique. It is capable of selecting proportions of mixing water, cement, supplementary cementitious materials, aggregates and superplasticizer, considering the effects of air content as well as water contributed by superplasticizer and moisture conditions of aggregates. Similar to most expert systems, this system has explanation facilities, can be incrementally expanded, and has an easy to understand knowledge base. The system was tested using a sample project. The system's selection of mix proportions and recommendations regarding mix adjustment were compared favourably with those of experts. The system is user-friendly and can be used as an educational tool.

A knowledge-based system for liquid retaining structure design with blackboard architecture

Owing to the high degree of vulnerability of liquid retaining structures to corrosion problems, there are stringent requirements in its design against cracking. In this paper, a prototype knowledge-based system is developed and implemented for the design of liquid retaining structures based on the blackboard architecture. A commercially available expert system shell VISUAL RULE STUDIO working as an ActiveX Designer under the VISUAL BASIC programming environment is employed. Hybrid knowledge representation approach with production rules and procedural methods under object-oriented programming are used to represent the engineering heuristics and design knowledge of this domain. It is demonstrated that the blackboard architecture is capable of integrating different knowledge together in an effective manner. The system is tailored to give advice to users regarding preliminary design, loading specification and optimized configuration selection of this type of structure. An example of application is given to illustrate the capabilities of the prototype system in transferring knowledge on liquid retaining structure to novice engineers.

A framework of knowledge versioning management

Abstract: Knowledge is an inherently dynamic entity continuously changing and evolving. In many cases, the coexistence of different versions of the same core knowledge is a necessity. So is the availability of the proper environment and tools to deal with knowledge versioning. In this paper, a framework of knowledge versioning management is proposed and implemented dealing with hybrid knowledge representation models using frames and rules. This framework facilitates knowledge version handling and maintenance, improving, in parallel, knowledge sharing and reuse. Knowledge components are stored in a set of tables and handled as data under the auspices of a database management system. The proper structure of tables and their relationships allows the creation of independent knowledge modules. Several knowledge modules can be assembled to construct higher level modules, which finally form versions of knowledge. Corresponding knowledge base versions consist of several knowledge modules easy to handle and process in various application areas. The proposed framework has been implemented and thoroughly examined in an application area of great importance, such as pest management.

Hybrid knowledge representation in a blackboard KBS for liquid retaining structure design

This paper highlights the importance of design expertise, for designing liquid retaining structures, including subjective judgments and professional experience. Design of liquid retaining structures has special features different from the others. Being more vulnerable to corrosion problem, they have stringent requirements against serviceability limit state of crack. It is the premise of the study to transferring expert knowledge in a computerized blackboard system. Hybrid knowledge representation schemes, including production rules, object-oriented programming, and procedural methods, are employed to express engineering heuristics and standard design knowledge during the development of the knowledge-based system (KBS) for design of liquid retaining structures. This approach renders it possible to take advantages of the characteristics of each method. The system can provide the user with advice on preliminary design, loading specification, optimized configuration selection and detailed design analysis of liquid retaining structure. It would be beneficial to the field of retaining structure design by focusing on the acquisition and organization of expert knowledge through the development of recent artificial intelligence technology.

Expert system application on preliminary design of water retaining structures

Design of liquid retaining structures involves many decisions to be made by the designer based on rules of thumb, heuristics, judgment, code of practice and previous experience. Various design parameters to be chosen include configuration, material, loading, etc. A novice engineer may face many difficulties in the design process. Recent developments in artificial intelligence and emerging field of knowledge-based system (KBS) have made widespread applications in different fields. However, no attempt has been made to apply this intelligent system to the design of liquid retaining structures. The objective of this study is, thus, to develop a KBS that has the ability to assist engineers in the preliminary design of liquid retaining structures. Moreover, it can provide expert advice to the user in selection of design criteria, design parameters and optimum configuration based on minimum cost. The development of a prototype KBS for the design of liquid retaining structures (LIQUID), using blackboard architecture with hybrid knowledge representation techniques including production rule system and object-oriented approach, is presented in this paper. An expert system shell, Visual Rule Studio, is employed to facilitate the development of this prototype system. (C) 2002 Elsevier Science Ltd. All rights reserved.

Research on hybrid expert system application to blanking technology

This paper introduces the application of a second generation expert system in blanking technology. Depending on the empirical knowledge of domain experts and field literature, an expert system can play an important role in the field of blanking technology. An object-oriented blackboard model is given which can realize hybrid knowledge representation, such as production rules, frameworks and procedures. This paper mainly discusses the structure of the blackboard model and hybrid knowledge representation in blanking technology expert system. Since strip layout is a crucial and determining part in blanking technology, given here is an example of a blackboard model supporting hybrid knowledge representation about the strip layout process.