The ability to remember past experiences enables a system to improve its performance as well as its competence. For example, a system might be able solve problems faster by adapting previous solutions. Additional tasks, such as avoiding unwanted behavior by detecting potential problems, monitoring long-term goals by remembering what subgoals have been achieved, and reflection on past actions, become feasible. As the tasks that an intelligent system accomplishes become more and more complex, so does the experience it acquires in the process. Such experience has a temporal extent and is expressed in terms of concepts and relations with deep semantics associated to them. Memory systems should be able to deal with the temporal aspect of experience, exploit this semantic knowledge for storage and retrieval and do so in a scalable fashion. However, relying just on experience will not achieve a broad coverage, as it needs to be used in conjunction with other reasoning mechanisms. That is why we need the ability to add episodic memory functionality to intelligent systems. Today's knowledge-based systems are complex software applications and the ability to develop them in a modular fashion, using generic, reusable components is essential. We propose to separate the episodic memory from the system that uses it and to build a generic, reusable memory module that can be attached to a variety of applications in order to provide this functionality. Its goal is to provide accurate, scalable, efficient and content-addressable access to prior episodes. Having such a reusable memory module should allow research to focus on the generic aspects of memory representation, organization and retrieval and its interaction with the external application and it should also reduce the complexity of the overall system. In this dissertation we propose a set of general requirements that any memory module should provide regarding memory encoding, storage and retrieval. We present an implementation that satisfies these requirements and evaluate it on three different tasks: plan synthesis, plan recognition and Physics problem solving. The memory module proved easily adaptable to these tasks, providing fast, accurate and scalable retrieval.

No description available.

Since MYCIN, explanation has become a fundamental feature of knowledge-based systems (KBS). Among the common deficiencies of KBS explanations, the most acute one is the lack of knowledge. This dissertation research investigates the use of explanations provided with hypertext for increasing the availability and accessibility of domain knowledge. The ultimate objective is to determine the behavioral and cognitive basis of the use of hypertext in providing KBS explanations. Two informationally equivalent KBS were comparatively studied in a laboratory setting: one used hypertext to provide explanations, while the other one used conventional lineartext. The experiment involved 26 experienced professionals, and 29 undergraduate and graduate students specializing in accounting. Subjects used the experimental KBS to work on a realistic problem of financial analysis. Both the process and outcomes of explanation use were assessed. Outcome variables included improvement in decision accuracy, trust in the KBS, and perceived usefulness of explanations. In addition to questionnaires used to measure decision accuracy and perceptions, computer logs were used to capture the number, type, and context of explanation use. Thinkingaloud procedures were used to assess the nature of explanation use. Results indicate that the use of hypertext for providing explanations significantly improved decision accuracy, and influenced users' preference for explanation types, and the number and context of explanation requests. Enhanced accessibility to deep explanations via the use of hypertext significantly increased the number of deep explanations requested by both novices and experts. Verbal protocol analysis shows that the lack of knowledge and means of accessing deep explanations could make it difficult to understand KBS recommendations, and that deep explanations could improve the understandability of KBS advice, especially in cases where unfamiliar domain concepts were involved. In the hypertext group, about 37% of the deep explanations were requested in the context of judgment making, rather than in the abstract. While only about 28% of the deep explanations requested by the lineartext group were the How type, 42% were the How type for the hypertext group. Experts and novices had different preferences for explanation types. Experts requested a much higher percentage of How, and lower percentages of Why and Strategic explanations, than novices. Verbal protocol analysis illustrates that experts and novices used explanations for different purposes.

No description available.

No description available.

Inference under uncertainty plays a crucial role in expert system and receives growing attention from artificial intelligence experts, statisticians, and psychologists. In searching for new satisfactory ways to model inference under uncertainty, it will be necessary to combine the efforts of researchers from different areas. It is expected that with deep insight into this crucial problem, it will not only have enormous impact on development of AI and expert system, but also bring classical areas like statistics into a new stage. This research paper gives a precise synopsis of present work in the field and explores the mechanics of statistical inference to a new depth by combining efforts of computer scientists, statisticians, and psychologists. One important part of the paper is the comparison of different paradigms, including the difference between statistical and logical views. Special attentions, which need to be paid when combining various methods, are considered in the paper. Also, some examples and counterexamples will be given to illustrate the availability of individual model which describes human behavior. Finally, a new framework to deal with uncertainty is proposed, and future trends of uncertainty management are projected. / Department of Mathematical Sciences

The continuous increase in the cost of building real life projects, the high cost of researches concerning a project and the urgent demands for the project to be delivered in a short period of time have urged researchers to find a new scheme of programming in which they would be able to simulate or emulate the real life activities and projects using a computer and a procedural language. But such a scheme of programming was lengthy, tedious and costly. Therefore, it was necessary to find a specialized simulation software that would save the programmer's time and effort on the one hand and would save the client's money on the other. A number of simulation software were developed recently which contributed greatly to the solutions of the simulation problems. However, none of these languages possessed the completeness nor the independence of the use of other tools or procedural languages to compensate for their deficiencies.In this paper I have proposed a number of new ideas aimed to improve the simulation languages in general, and have implemented a number of these ideas in a software package. Chapter one describes the simulation model and the basic concepts of simulation. Chapter two describes briefly the advantages and disadvantages of using simulation software. Also it outlines the limitations of currently used simulation packages. Finally, it suggests new ideas and expectations of a complete simulation package. Chapter three describes the simulation package prototype S. E. S. and gives some implementations. Chapter four explains the basic differences between S. E. S. and SLAM II and shows areas for further research. / Department of Computer Science

No description available.

Doctor-patient partnership has been advocated for achieving effective diabetes management. Such a partnership requires empowering patients with proper knowledge and skills so they are capable of participating in decision-making, effective communication with health professionals and successful diabetes self-management. Although it is well known that patient education should be tailored and prioritised, little research on Computer-based Patient Education Systems (CPESs) has aimed at customising information that is flexible enough to adapt to firstly, the dynamic nature of patients' ongoing information needs and secondly, changes in their personal health and social circumstances. Moreover, except for indirect support for doctor-patient communication through tailored information, current CPESs do not aid patients in formulating their questions. / This thesis targets limitations of current CPESs and explores approaches for using information technology (IT) to support the doctor-patient partnership. Two approaches used to achieve the research goal are 1) providing essential information to individual patients - information that is not only relevant, but also prioritised; and 2) providing direct support for patients to generate personalised agendas prior to scheduled health visits. The innovative technologies that have been developed for implementing these two approaches include a comprehensive Diabetes Information Profile (DIP) for each patient, information tailoring and prioritisation algorithms (information algorithms), quiz tailoring and prioritisation algorithms (quiz algorithms), and agenda personalisation algorithms (which serve to populate an agenda question pool). The DIP includes data elements on a patient's lifestyle, diet profile, psychosocial profile, risk factors of diabetic complications, behaviour change profile, self-management profile, and clinical status. The information algorithms take into account these DIP elements, as well as patients' diabetes knowledge level (based on educational exposure) and individual information preferences. Collectively, the implementation of these approaches, using an extensible architecture based on Extensible Mark-up Language (XML) and Java technologies, is called “Violet Technology” (VT). A VT-based web portal has been developed and evaluated. / A two-phase evaluation was conducted through the Diabetes Centre of a metropolitan hospital. The first study evaluates the validity of the information algorithms through patient and healthcare provider assessment of prioritised information topics. The participants of the first study include 11 patients with diabetes, one General Practician (GP), one endocrinologist, two diabetes nurse educators and one dietician. The second trial evaluates the VT-based portal overall - including information, quiz and agenda personalisation algorithms - through a field trial of the portal with random selection of patients to treatment and control groups. In total, 27 patients, one GP, one endocrinologist, two nurse educators, and one dietician were involved in the second trial. The evaluations provide qualitative support for the relevance of information prioritisation by VT, and show acceptable consumer usability, as well as healthcare provider support, for the portal. The evaluations also revealed further incremental refinements to the information algorithms. / This thesis contributes a specific framework for the use of IT to support the doctor-patient partnership through prioritised information and integrated agenda formulation services. While a larger scale of evaluation is needed to establish patient health benefits, the results of the two initial studies are encouraging. This framework could be adapted for other chronic diseases, such as depression or asthma. It could also be used for other purposes, such as an intelligent information searching facility. A future VT framework should provide more explicit representation of patients' emotional supports and have further mechanisms for promoting patient behaviour changes. / Thesis (PhDInformationTechnology)--University of South Australia, 2005.

Thesis (MEng in Electronic Engineering (Research)) -- University of South Australia, 1995