Declarative Knowledge Representation Research Articles

Cognitive Tools for Abstract Thinking Autonomous Intelligent Mobile Systems

The actual problems of artificial intelligence related to the development of tools for abstract thinking of autonomous intelligent mobile systems are being solved, which allow planning purposeful behavior in hard-to-reach and aggressive environments for humans. Cognitive tools are proposed that provide intelligent systems with the ability to organize purposeful multi-stage activities related to solving complex problems, when a behavior plan is automatically built in some conditions of a problematic environment, and a given behavior goal is achieved in other operating conditions that are beyond the resolution of technical vision. An important feature of the proposed typical elements of knowledge representation and processing is that they allow intelligent systems to organize the output of solving complex problems, relying only on the data stored in the knowledge representation model and coming from the current operating conditions. In the general case, the developed knowledge model of intelligent systems for various purposes consists of declarative and procedural typical elements of their representation. For a formal description of typical elements of declarative knowledge representation, traditional semantic networks and various sets of restrictions are used, reflecting additional conditions for the future functioning of autonomous mobile intelligent systems. As for the formal description of the typical elements of the representation of procedural knowledge, regardless of a specific subject area, fuzzy semantic networks are used for this. This allows autonomous intelligent mobile systems to adapt to specific operating conditions in underdetermined problematic environments and perform complex tasks formulated by them on this basis. The practical significance of the results obtained lies in the effectiveness of their use for the development of intelligent problem solvers that provide autonomous intelligent mobile systems for various purposes with the ability to perform complex tasks in a priori underdetermined problematic environments by adapting the purposeful activity plan formed in general form to specific current operating conditions.

An Expert System for Screening and Prognosis of Diseases: An Instance of Healthcare Management

The background of this study is that a Medical Expert System made in Visual Prolog is proposed. This expert system makes a differential diagnosis among heart, lung, kidney, skin, and brain diseases. This system is designed to give help to a medical expert in making the appropriate diagnosis of a patient. Based on a patient's symptoms and medical background, SWI Prolog offers the diagnosis through the declarative knowledge representation methodologies. On the basis of the diagnosis and current medical regulations, it might also offer other treatment options. Medical data analysis using SWI Prolog is used to spot trends or patterns in patient outcomes or disease development. Making better-educated choices concerning patient care and treatment could be made easier by healthcare providers as a result. An SWI Prolog-based medical expert system's output will be influenced by the quality of the data and code used to generate it, as well as by the medical specialists that worked on its design and implementation. The creation of more sophisticated expert systems can be particularly beneficial for early disease detection, helping to reduce the burden of diseases by detecting them more accurately and efficiently.

The third AI summer: AAAI Robert S. Engelmore Memorial Lecture

The third AI summer: AAAI Robert S. Engelmore Memorial Lecture

Model of Representation and Acquisition of New Knowledge by an Autonomous Intelligent Robot Based on the Logic of Conditionally Dependent Predicates

The model for the representation of declarative and procedural knowledge of an autonomous intelligent robot is developed without reference to a specific subject area. The logic of conditionally dependent predicates underlies the construction of this model. Procedures that allow an autonomous intelligent robot to automatically generate new knowledge needed for a readout in the process of planning goal-seeking behavior in undetermined conditions of a problem-solving environment are proposed. The method of proving the satisfiability of the formulas under the logic of conditionally dependent predicates with linear complexity is based on the attribution of object variables in them as objects of the problem-solving environment and serves to process knowledge that is used by an autonomous intelligent robot to automatically build plans for goal-seeking behavior under undetermined operating conditions.

Answer Set Programming in Linguistics

This survey collects scientific works where answer set programming, a declarative knowledge representation and reasoning formalism, is applied to natural language processing and computational linguistics.

Arabic Ontology Model for Financial Accounting

Abstract Ontologies have become core components of many large applications. It's part of the Semantic Web framework. Many disciplines now develop standardized ontologies that domain experts can use to share and annotate information in their fields. Arabic is still not well supported. Designing and developing Arabic Ontologies need more than what is provided by keyword-based and more than analysing the morphology and grammar of the traditional Arabic language A few works on Arabic Semantic Web applications are derived from the traditional Arabic language and not in the specialized science. However, available search engines supporting Arabic language are typically limited to keyword searches and do not take in consideration the underlying semantics of the content. In this research, we propose a model for representing Arabic financial accounting knowledge in the Computer Technology domain using Ontologies. This paper addresses an Arabic financial ontology and presents a methodology for creating ontologies based on declarative knowledge representation systems.

Key Properties of Expert Movement Systems in Sport

This paper identifies key properties of expertise in sport predicated on the performer-environment relationship. Weaknesses of traditional approaches to expert performance, which uniquely focus on the performer and the environment separately, are highlighted by an ecological dynamics perspective. Key properties of expert movement systems include 'multi- and meta-stability', 'adaptive variability', 'redundancy', 'degeneracy' and the 'attunement to affordances'. Empirical research on these expert system properties indicates that skill acquisition does not emerge from the internal representation of declarative and procedural knowledge, or the imitation of expert behaviours to linearly reduce a perceived 'gap' separating movements of beginners and a putative expert model. Rather, expert performance corresponds with the ongoing co-adaptation of an individual's behaviours to dynamically changing, interacting constraints, individually perceived and encountered. The functional role of adaptive movement variability is essential to expert performance in many different sports (involving individuals and teams; ball games and outdoor activities; land and aquatic environments). These key properties signify that, in sport performance, although basic movement patterns need to be acquired by developing athletes, there exists no ideal movement template towards which all learners should aspire, since relatively unique functional movement solutions emerge from the interaction of key constraints.

The Answer Set Programming Competition

The Answer Set Programming (ASP) Competition is a biannual event for evaluating declarative knowledge representation systems on hard and demanding AI problems. The competition consists of two main tracks: the ASP system track and the model and solve track. The traditional system track compares dedicated answer set solvers on ASP benchmarks, while the model and solve track invites any researcher and developer of declarative knowledge representation systems to participate in an open challenge for solving sophisticated AI problems with their tools of choice. This article provides an overview of the ASP Competition series, reviews its origins and history, giving insights on organizing and running such an elaborate event, and briefly discusses the lessons learned so far.

Repairing Access Control Configurations via Answer Set Programming

Abstract Although various access control models have been proposed, access control configurations are error prone. There is no assurance of the correctness of access control configurations. When we find errors in an access control configuration, we take immediate actions to repair the configuration. The repairing is difficult, largely because arbitrary changes to the configuration may result in no less threat than errors do. In other words, constraints are placed on the repaired configuration. The presence of constraints makes a manual error-and-trial approach less attractive. There are two main shortcomings with the manual approach. Firstly, it is not clear whether the objectives are reachable at all; if not, we waste time trying to repair an error prone configuration. Secondly, we have no knowledge of the quality of the solution such as correctness of the repair. In order to address these shortcomings, we aim to develop an automated approach to the repairing task of access control configurations. We have utilized answer set programming (ASP), a declarative knowledge representation paradigm, to support such an automated approach. The rich modeling language of ASP enables us to capture and express the repairing objectives and the constraints. In our approach, the repairing instance is translated into an ASP, and the ASP solvers are invoked to evaluate it. Although the applications of ASP follow the general “encode-compute-extract” approach, they differ in the representations of the problems in ASP. In our case, there are two principal factors which render the proposed problem and approach non-trivial. Firstly, we need to identify constraints which are not only amendable to ASP interpretation, but also expressive enough to capture common idioms of security and business requirements; there is a trade-off to make. Secondly, our ASP programs should model the quality measure of repairs—when more than one repair is possible, the reported one is optimized in terms of the quality measure. We have also undertaken extensive experiments on both real-world and synthetic data-sets. The experiment results validate the effectiveness and efficiency of the automated approach.

Planning with Specialized SAT Solvers

Logic, and declarative representation of knowledge in general, have long been a preferred framework for problem solving in AI. However, specific subareas of AI have been eager to abandon general-purpose knowledge representation in favor of methods that seem to address their computational core problems better. In planning, for example, state-space search has in the last several years been preferred to logic-based methods such as SAT. In our recent work, we have demonstrated that the observed performance differences between SAT and specialized state-space search methods largely go back to the difference between a blind (or at least planning-agnostic) and a planning-specific search method. If SAT search methods are given even simple heuristics which make the search goal-directed, the efficiency differences disappear.

Syllabus Management System for Academics Practicing Knowledge Management

Knowledge management, a very popular term which describes a range of practices used by organizations to identify, create, represent, and distribute knowledge for reuse, awareness and learning across the organization. This paper will discuss a practice used by academics in Centre for Diploma Programme (CDP) in Multimedia University via syllabus management system. Every two to five years, CDP’s academic and management staff will prepare the updated syllabi for MQA accreditation. The system will assist both the administrator and the lecturers in organizing, updating and retrieving their syllabi information for the entire Diploma in Information Technology (DIT) programme. There are basically a few factors that encourage academics to practice knowledge management. First of all, we will identify the contributing factors that encourage the academics in CDP, MMU for practicing knowledge management i.e. MQA’s requirement, academics’ commitment and the university’s requirement and then the practice of knowledge management is shared through the Syllabus Management System. The requirement models have been represented using the Unified Modeling Language (UML) and the development stage uses the ontology development methodology. The ontology methodology is then used as a guideline for creating ontologies based on a declarative knowledge representation system. The system is deployed on the Protege ontology editor tool.

Use artificial neural network to align biological ontologies

BackgroundBeing formal, declarative knowledge representation models, ontologies help to address the problem of imprecise terminologies in biological and biomedical research. However, ontologies constructed under the auspices of the Open Biomedical Ontologies (OBO) group have exhibited a great deal of variety, because different parties can design ontologies according to their own conceptual views of the world. It is therefore becoming critical to align ontologies from different parties. During automated/semi-automated alignment across biological ontologies, different semantic aspects, i.e., concept name, concept properties, and concept relationships, contribute in different degrees to alignment results. Therefore, a vector of weights must be assigned to these semantic aspects. It is not trivial to determine what those weights should be, and current methodologies depend a lot on human heuristics.ResultsIn this paper, we take an artificial neural network approach to learn and adjust these weights, and thereby support a new ontology alignment algorithm, customized for biological ontologies, with the purpose of avoiding some disadvantages in both rule-based and learning-based aligning algorithms. This approach has been evaluated by aligning two real-world biological ontologies, whose features include huge file size, very few instances, concept names in numerical strings, and others.ConclusionThe promising experiment results verify our proposed hypothesis, i.e., three weights for semantic aspects learned from a subset of concepts are representative of all concepts in the same ontology. Therefore, our method represents a large leap forward towards automating biological ontology alignment.

An ontological knowledge framework for adaptive medical workflow

As emerging technologies, semantic Web and SOA (Service-Oriented Architecture) allow BPMS (Business Process Management System) to automate business processes that can be described as services, which in turn can be used to wrap existing enterprise applications. BPMS provides tools and methodologies to compose Web services that can be executed as business processes and monitored by BPM (Business Process Management) consoles. Ontologies are a formal declarative knowledge representation model. It provides a foundation upon which machine understandable knowledge can be obtained, and as a result, it makes machine intelligence possible. Healthcare systems can adopt these technologies to make them ubiquitous, adaptive, and intelligent, and then serve patients better. This paper presents an ontological knowledge framework that covers healthcare domains that a hospital encompasses—from the medical or administrative tasks, to hospital assets, medical insurances, patient records, drugs, and regulations. Therefore, our ontology makes our vision of personalized healthcare possible by capturing all necessary knowledge for a complex personalized healthcare scenario involving patient care, insurance policies, and drug prescriptions, and compliances. For example, our ontology facilitates a workflow management system to allow users, from physicians to administrative assistants, to manage, even create context-aware new medical workflows and execute them on-the-fly.

Inferring Phylogenetic Trees Using Answer Set Programming

We describe the reconstruction of a phylogeny for a set of taxa, with a character-based cladistics approach, in a declarative knowledge representation formalism, and show how to use computational m...

KASER: knowledge amplification by structured expert randomization

In this paper and attached video, we present a third-generation expert system named Knowledge Amplification by Structured Expert Randomization (KASER) for which a patent has been filed by the U.S. Navy's SPAWAR Systems Center, San Diego, CA (SSC SD). KASER is a creative expert system. It is capable of deductive, inductive, and mixed derivations. Its qualitative creativity is realized by using a tree-search mechanism. The system achieves creative reasoning by using a declarative representation of knowledge consisting of object trees and inheritance. KASER computes with words and phrases. It possesses a capability for metaphor-based explanations. This capability is useful in explaining its creative suggestions and serves to augment the capabilities provided by the explanation subsystems of conventional expert systems. KASER also exhibits an accelerated capability to learn. However, this capability depends on the particulars of the selected application domain. For example, application domains such as the game of chess exhibit a high degree of geometric symmetry. Conversely, application domains such as the game of craps played with two dice exhibit no predictable pattern, unless the dice are loaded. More generally, we say that domains whose informative content can be compressed to a significant degree without loss (or with relatively little loss) are symmetric. Incompressible domains are said to be asymmetric or random. The measure of symmetry plus the measure of randomness must always sum to unity.

Consistency-based diagnosis of configuration knowledge bases

Configuration problems are a thriving application area for declarative knowledge representation that currently experiences a constant increase in size and complexity of knowledge bases. Automated support of the debugging process of such knowledge bases is a necessary prerequisite for effective development of configurators. We show that this task can be achieved by consistency-based diagnosis techniques. Based on the formal definition of consistency-based configuration we develop a framework suitable for diagnosing configuration knowledge bases. During the test phase of configurators, valid and invalid examples are used to test the correctness of the system. In case such examples lead to unintended results, debugging of the knowledge base is initiated. Starting from a clear definition of diagnosis in the configuration domain we develop an algorithm based on conflicts. Our framework is general enough for its adaptation to diagnosing customer requirements to identify unachievable conditions during configuration sessions. A prototype implementation using commercial constraint-based configurator libraries shows the feasibility of diagnosis within the tight time bounds of interactive debugging sessions. Finally, we discuss the usefulness of the outcomes of the diagnostic process in different scenarios.

Declarative representation of strategic control knowledge

Strategic (control) knowledge typically specifies how a target task is solved. Representing such knowledge declaratively remains a difficult and practical knowledge engineering challenge. The key to addressing this challenge rests on two observations. One, strategic knowledge comprises two finer types of knowledge: subgoaling knowledge used to construct the goal structure for each problem situation pertaining to a target task, and goal-sequencing knowledge used to choose which subgoal in this goal structure is to be pursued at any given moment. Second, when subgoaling knowledge is explicit and expressed in declarative ontological terms, it is possible to fully express goal-sequencing knowledge in the same declarative terms. Building on these observations, we achieve three things. First, we analyse several conventional knowledge-based applications whose subgoaling and goal-sequencing knowledge is implicit, showing that making their subgoaling knowledge explicit permits (re)representing their goal-sequencing knowledge declaratively. Among the applications analysed are MORE and NEOMYCIN. Second, upon studying the roles of goal-sequencing knowledge vis-á-vis subgoaling knowledge, we develop a declarative formalism for representing goal-sequencing knowledge. Finally, we discuss and illustrate key benefits from using our declarative formalism, including an enhanced ability to validate and reuse goal-sequencing knowledge.

Pharmacophore Discovery Using the Inductive Logic Programming System PROGOL

This paper presents a case study of a machine-aided knowledge discovery process within the general area of drug design. Within drug design, the particular problem of pharmacophore discovery is isolated, and the Inductive Logic Programming (ILP) system progol is applied to the problem of identifying potential pharmacophores for ACE inhibition. The case study reported in this paper supports four general lessons for machine learning and knowledge discovery, as well as more specific lessons for pharmacophore discovery, for Inductive Logic Programming, and for ACE inhibition. The general lessons for machine learning and knowledge discovery are as follows. 1. An initial rediscovery step is a useful tool when approaching a new application domain. 2. General machine learning heuristics may fail to match the details of an application domain, but it may be possible to successfully apply a heuristic-based algorithm in spite of the mismatch. 3. A complete search for all plausible hypotheses can provide useful information to a user, although experimentation may be required to choose between competing hypotheses. 4. A declarative knowledge representation facilitates the development and debugging of background knowledge in collaboration with a domain expert, as well as the communication of final results.

A data/knowledge paradigm for the modeling and design of operations support systems

The paper develops the Smart Object paradigm and its instantiation, which provide a new conceptualization for the modeling, design, and development of an important but little researched class of information systems, operations support systems (OSS). OSS is the authors' term for systems which provide interactive support for the management of large, complex operations environments, such as manufacturing plants, military operations, and large power generation facilities. The most salient feature of an OSS is its dynamic nature. The number and kind of elements composing the system as well as the mode of control of those elements change frequently in response to the environment. The abstraction of control and the ease with which complex dynamic control behavior can be modeled and simulated is one of the important aspects of the paradigm. The framework for the Smart Object paradigm is the fusion of object-oriented design models with declarative knowledge representation and active inferencing from AI models. Additional defining concepts from data/knowledge models, semantic data models, active databases, and frame based systems, are added to the synthesis as justified by their contribution to the ability to naturally model OSS at a high level of abstraction. The model assists in declaratively representing domain data/knowledge and its structure, and task or process knowledge, in addition to modeling multilevel control and interobject coordination.

Representation of human-computer interaction by means of behavior rules

This article proposes the application of techniques from knowledge engineering to the development of graphic interfaces. The current paradigm of knowledge-based systems is rooted in features such as declarative representation of knowledge, design modularity, and independence between data, knowledge, and control. The main advantage of this approach with respect to conventional programming techniques is that the incremental development of interfaces, as well as their modification and maintenance, is made easier. We present a human-machine interaction technique that seeks a compromise between simplicity and expressive power.