Representation Of Procedural Knowledge Research Articles

Автоматическое построение элементов представления процедурных знаний подводным интеллектуальным роботом в условиях недоопределенности

В статье рассматриваются и решаются проблемы, связанные с развитием одного из подходов к построению модели представления процедурных знаний интеллектуального робота в виде заданного набора фрейм-микропрограмм поведения. В общем случае фрейм-микропрограммы представляют собой навыки поведения, связанные с решением соответствующих им подзадач безотносительно к конкретной предметной области, обеспечивающие роботу возможность автоматического формирования планов целенаправленной деятельности, связанных с решением сложных задач в различных условиях проблемной среды. Иначе говоря, фрейм-микропрограммы поведения являются “кирпичиками”, на основе которых интеллектуальный робот может конструировать сложные планы целенаправленной деятельности в различных условиях функционирования. В развитие данной модели представления процедурных знаний робота предложена структура фрейм-микропрограммы поведения, в результате декомпозиции которой можно получить модули, определяющие относительно простые навыки поведения. Такой подход позволяет роботу организовать либо более гибкий процесс планирования целенаправленной деятельности в недоопределенных проблемных средах, либо обеспечить интеллектуального робота возможностью автоматического построения недостающих для достижения заданной цели типовых элементов представления процедурных знаний. Выбор конкретного из данных двух способов использования полученных в результате разбиения фрейм-микропрограмм поведения модулей зависит от сложности решаемой интеллектуальным роботом задачи и размерности семантической сети, описывающей текущую ситуацию проблемной среды. Определены условия, обеспечивающие выбор результативных модулей представления знаний в процессе планирования целенаправленной деятельности и автоматического построения недостающих для вывода решений фрейм-микропрограмм поведения. Показано, что предложенный подход к построению модели представления процедурных знаний и инструментальных средств их обработки в процессе планирования целенаправленной деятельности позволяет повысить когнитивные возможности интеллектуальных роботов и на этой основе обеспечить решение сложных задач в недоопределенных проблемных средах. The article discusses and solves problems associated with the development of one of the approaches to constructing a model for representing the procedural knowledge of an intelligent robot in the form of a given set of behavioral frame-microprograms. In general, frame microprograms represent behavioral skills associated with solving the corresponding subtasks, without reference to a specific subject area, providing the robot with the ability to automatically generate plans for goal-directed activities related to solving complex problems in various environmental conditions. In other words, frame-microprograms of behavior are the “building blocks” on the basis of which an intelligent robot can construct complex plans for purposeful activity in various operating conditions. In development of this model for representing a robot's procedural knowledge, a structure of a frame-microprogram is proposed, as a result of the decomposition of which it is possible to obtain modules that define relatively simple behavioral skills. This approach allows the robot to organize either a more flexible process of planning purposeful activities in underdetermined problem environments, or to provide the intelligent robot with the ability to automatically construct typical elements of procedural knowledge representation that are missing to achieve a given goal. The choice of a particular one of these two methods of using the modules obtained as a result of splitting the behavioral frame-microprograms depends on the complexity of the task being solved by the intelligent robot and the dimension of the semantic network describing the current situation of the problem environment. The conditions have been determined to ensure the identification and selection of effective knowledge representation modules in the process of planning targeted activities and the automatic construction of behavioral frame-microprograms that are missing for the conclusion of decisions. It is shown that the proposed approach to constructing a model for representing procedural knowledge and tools for processing it in the process of planning purposeful activities makes it possible to increase the cognitive capabilities of intelligent robots and, on this basis, provide solutions to complex problems in underdetermined problem environments.

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.

Neural Representations of Procedural Knowledge

Although declarative concepts (e.g., apple) have been shown to be identifiable from their functional MRI (fMRI) signatures, the correspondence has yet to be established for executing a complex procedure such as tying a knot. In this study, 7 participants were trained to tie seven knots. Their neural representations of these seven procedures were assessed with fMRI as they imagined tying each knot. A subset of the trained participants physically tied each knot in a later fMRI session. Findings demonstrated that procedural knowledge of tying a particular knot can be reliably identified from its fMRI signature, and such procedural signatures were found here in frontal, parietal, motor, and cerebellar regions. In addition, a classifier trained on mental tying signatures was able to reliably identify when participants were planning to tie knots before they physically tied them, which suggests that the mental-tying and physical-tying procedural signatures are similar. These findings indicate that fMRI activation patterns can illuminate the representation and organization of procedural knowledge.

Multi-level medical knowledge formalization to support medical practice for chronic diseases

Medical processes combine medical actions which are performed by health care professionals while they observe signs and symptoms and decide about interventions, prescriptions, or tests, in order to deal with the health problem that affects a particular patient.Our research was centered in the representation of knowledge for the purpose of decision making in medical processes for chronic diseases. In order to achieve this objective, we followed three steps: (1) We performed an analysis and comparison of formal languages for procedural knowledge representation from a decision-making perspective. (2) We proposed an intuitive, easy, and effective mechanism of medical knowledge formalization. And, (3) we defined a methodology to model medical processes. Our new formalism to represent knowledge is called the extended Timed Transition Diagram (eTTD) and can be used to describe three basic levels of decision making in a long-term treatment: therapy strategy, dosage, and intolerances. The methodology can be applied manually to build eTTDs from clinical practice guidelines (CPGs) or automatically to construct them using the information available in clinical records about individual, multi-level medical processes.We validated eTTDs with clinical practice guidelines for arterial hypertension. The obtained models can be used as a baseline framework for medical, procedural decision support systems development.

Causal design knowledge: Alternative representation method for product development knowledge management

This paper presents a mathematical comparison of procedural knowledge and causal knowledge, and discusses the potential roles and feasibility of causal knowledge across product development knowledge management. Since reuse of knowledge is so important in product development, various knowledge management approaches have been introduced. Most of the product design knowledge is represented by procedural knowledge, which unfortunately requires cumbersome processes to define, and is typically inadequate for representing the kind of knowledge generated during the product development process. A causal knowledge representation, however, can help us to overcome this limitation and is an alternative formalism for representing product design knowledge. In this paper we compare the procedural and causal knowledge representations. We present the mathematical definitions of two knowledge paradigms, then mathematically describe the relationship between the two. Both knowledge paradigms are then compared based on the perspective of knowledge expression, decision alternative representation, reasoning capability, and knowledge cultivation. This paper concludes that causal knowledge representation is superior to procedural knowledge representation based on the four perspectives. Finally, the knowledge systems are modeled using Systems Modeling Language (SysML), and we present a case study that demonstrates the causal knowledge features using a realistic example from industry.

Workflow management in the grid era: A goal-driven approach based on process patterns

Workflow management in the grid era faces many challenges both related to workflow modeling and workflow execution. To deal with the challenges, the paper argues that the traditional process-driven workflow management paradigm should be replaced by a goal-driven one and proposes a corresponding implementation approach. The contributions of the paper are as follows: (1) a concept of process pattern is put forward that is an effective means not only for procedural knowledge representation and sharing but also for expertise exchange between domain experts and computer scientists; (2) based on process patterns, a goal-driven workflow framework is designed that incorporates semantic Web technologies and artificial intelligent (AI) planning techniques and that can (semi-)automatically generate a workflow on the fly according to the requirements of users and the running context.

On the representation of expert procedural knowledge

Procedural knowledge is used by experts for complex system control. In this article, the notion of a complex system is taken in a broad sense. It might be a patient cured by a physician specialist, a biotechnological device, a department of some business enterprise etc. The GLIF model was designed in collaboration of American universities for the formalization of medical guidelines, but it can be used for formal representation of any procedural knowledge. The main objective of the GLIF model was to enable computer processing and comparing of medical guidelines. In this, article also a more sophisticated use of procedural knowledge representation by the means of the GLIF model is proposed. The provided data about expert actions are stored into the database, the formalized knowledge represented by the GLIF model can be used for building up sophisticated reminder systems that warn the user if he decides to make an impropriate action. Different kinds of warnings in the reminder system are proposed and their properties are discussed. At the end, also the possibility of using the GLIF model for decision support is discussed. 

Challenges in Using the Arden Syntax for Computer-Based Nosocomial Infection Surveillance

Context: Detection of outbreaks of infection in the hospital typically requires daily manual review of microbiology laboratory test results. This process is time-consuming, tedious, prone to error and may miss trends in infection. A standard formalism for procedural knowledge representation, the Arden Syntax, provides a vehicle for implementing algorithms for detecting such infections. Objective: To design and implement a computer-based system for detection of concerning patterns of infection or antibiotic resistance. Setting: Computer-based event monitor and central patient data repository at the Columbia-Presbyterian Medical Center (CPMC). Results: We designed a two-phase system, including initial filtering of individual patient laboratory results by Arden Syntax Medical Logic Modules (MLMs) and subsequent aggregation and analysis across patients and locations using a statistical monitor. Preliminary data for the filtration phase demonstrate a 94.8% reduction in the volume of messages that must be considered in surveillance. Conclusions: Filtering raw laboratory results using a standard formalism eases the process of aggregating data across patients and sites as well as detecting trends in infection. There is a need for augmenting such formalisms in order to enable population-based decision support.

AMS formalism

The Activity Manager System (AMS) formalism anchored on key concepts such as activity, action, activity network, state, and MOPA (Memory Organization Packets for Activities) is capable of modeling a broad spectrum of office activities, ranging from the structured to the ill-structured, in a rigorous and comprehensive manner. The formalism can also be used to guide the development of Office Information Systems (OISs). It is domain independent and is particularly suited for the hierarchical representation of procedural knowledge. The formalism has the following features: 1) As in the rule-based formalism, control is separated from knowledge, and programming is replaced by the explicit, declarative representation of packets of knowledge; 2) Using the activity network concept, we can obtain an explicit hierarchical representation of procedural knowledge; 3) The activity concept provides the formalism with a representation of a rule-like entity; and 4) The MOPA concept plays the role of organizing and reconstructing knowledge in an appropriate manner, and allows the dynamic generation of Activity Networks at various levels of abstraction. The formalism as a theoretical underpinning for the development of OISs has been demonstrated at Bull's Advanced Studies Laboratory by Ader (in developing the office model) and Li (in developing the AMS).

AMS: a declarative formalism for hierarchical representation of procedural knowledge

This paper presents a domain-independent formalism, called Activity Manager System (AMS), for the explicit, hierarchical representation of procedural knowledge. The formalism is able to: (1) describe procedures at a reasonable level of complexity and completeness; (2) generate dynamically procedures at different levels of abstraction; (3) organize knowledge hierarchies by means of abstract entities; and (4) allow knowledge reusability at different levels of abstraction. AMS uses the notions of activity, activity network, state, and memory organization packet for activities (MOPA) to represent abstractions, and it allows the user to model an application in a hierarchical manner. The advantages and limitations of AMS are also discussed.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Knowledge representation for problem processing in a decision support system

This paper examines the position and function of a “problem processing system” (PPS) in the context of a decision support system (DSS), and proposes a relatively complete framework for such a problem processing system. Using the concept of a “problem processing mode”, the paper introduces further research into the so-called “representation of procedural knowledge”, and presents a generalized structure for PPSs. This structure has been used in specific problem processing in a production-planning context.

Knowledge-based technology for solving complex systems of interrelated problems

The solution of a system of problems is modeled as a search among alternative paths on a semantic network. We consider representation of procedural knowledge and modeling of decision processes by production rules and Petri nets.

A model-based aid for monitoring and controlling a large-scale system

Approaches to aiding human performance in monitoring and controlling a large-scale system are considered. One particular approach that is based on a model of human performance is described in detail. The model includes a rule-based representation of procedural knowledge, a frame-based representation of declarative knowledge, and a fuzzy prioritization mechanism to handle rule of conflicts. The online aiding approach is implemented in a simulated large-scale system that requires human monitoring and control. Results from an experimental evaluation of the aid are inconclusive in that they do not demonstrate the benefit of a direct advice-giving system; however, the aiding approach was found helpful in some tasks.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

Representation of Procedural Knowledge by a Model of Human Associative Processor (HASP)

AbstractA human associative processor (HASP) proposed by the author was developed and applied to procedural knowledge. The first step is a simple positive‐feedback model in which the output of a conventional HASP is fed back to its own key input. The chain of a procedure is memorized by taking the positive‐feedback input as the key input of the association, and by associating with the next procedure. A series of computer‐simulation studies showed that the model could express control structures such as wait, jump, do‐while, if‐then, and case control procedures. The second step shows that the simple positive‐feedback model sometimes cannot read the memorized procedure correctly; then an improved model was proposed. In the improved model structure, a converted orthogonal pattern from the positive feedback point and the external input key are used again as the key input. The improved model can read all the memorized procedures correctly; this was confirmed by computer simulation. It was also shown that the pattern completion function associated with the whole procedure from its part, can be represented by noise environmentalization.

The acquisition of procedures from text: A production-system analysis of transfer of training

Learning a cognitive skill from written instructions can be viewed as consisting of converting the propositional content of the written material into a representation of procedural knowledge, such as production rules. In a transfer of training experiment, subjects learned from step-by-step instructions a series of related procedures, in different training orders, for operating a simple device. The strong between-procedure transfer effects were predicted by a simple model of transfer in which individual production rules can be transferred or re-used in the representation of a new procedure if they had been used in a previously learned procedure. Apparently, this transfer mechanism acts on declarative propositional representations of the production rules, suggesting that it is more similar to comprehension processes than to conventional practice mechanisms, or to Anderson's learning principles (1982, Psychological Review, 89, 369–406; 1983, The architecture of cognition, Cambridge, MA, Harvard Univ. Press).