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Intelligent tutoring systems (ITS) are expert systems united with computer-aided instruction. The psychological issue of human-computer interfacing combines aspects of education, cognitive science, human performance and psycho-sociolinguistics. This study presented a situation in which physicians used their reasoning to solve a computer-simulated medical case, embedded in the NEOMYCIN ITS. Experiments were designed to assess how their anthropomorphisation of the systems affected their medical reasoning in a complex ill-defined problem-solving domain. The study examines the subjects' interpretation of textual case materials, specifically their ascription of meaning and intelligibility to the form and usage of natural language. The results indicate that these factors affect their interpretation, not only of case materials, but also of their evaluation of the program's medical reasoning. This has implications for the interactive man-machine interface and its relationship to interpersonal communication is discussed.

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Prekiaujant vertybiniais popieriais, svarbiausia yra priimti teisingą sprendimą: pirkti arba parduoti. Daugelis investuotojų prieš priimdami sprendimą atkreipia dėmesį į pasirinktos akcijos kainos kitimo grafiką ir vadovaujasi juo. Tačiau ne kiekvienas investuotojas galėtų tiksliai apibūdinti savo pasirinktą grafinį modelį. Problemos aktualumas - Prognozuoti rinkas yra pakankamai sudėtinga, pastebimas žymus akcijų kursų svyravimas. Ženklūs akcijų kursų pasikeitimai skaičiuojami ne per metus ar mėnesius, o dienomis ar net valandomis. Investitoriams, finansų analitikams finansinėse rinkose sunku dirbti. Spekuliavimas akcijomis aktyviose akcijų rinkose yra labai rizikingas, bet pelningas užsiėmimas. Pasiūlius sprendimo priėmimo metodą investavimo procesas techniniu požiūriu supaprastės ir nereikalaus didelių sąnaudų, bei gilių žinių, leis platesniam ratui žmonių įeiti į akcijų rinką. Problema – Sudėtingas akcijų rinkų prognozavimas, kadangi pastebimas žymus akcijų kursų svyravimas, todėl rizikinga spekuliuoti akcijomis aktyviose akcijų rinkose. Baigiamojo darbo objektas – sprendimo priėmimo metodas finansinių rinkų prognozėms atlikti, remiantis neuroniniais tinklais ir spiečiaus algoritmu. Baigiamojo darbo tikslas – Spiečiaus intelekto taikymo finansų rinkose analizė ir optimizavimas. / One of the central problems in financial markets is to make the profitable stocks trading decisions using historical stocks' market data. This paper presents the decision-making methodology which is based on the application of neural networks and swarm intelligence technologies and is used to generate one-step ahead investment decisions. In brief, the proposed methodology draws from the analysis of historical stock prices variations. The variations are passed to neural networks and the recommendations for the next day are calculated. The stocks with the highest recommendations are considered for further experimental investigations. The core idea of this algorithm is to select three best neural networks for the future investment decisions and to adapt the weights of other networks towards the weights of the best network. The experimental results presented in the paper show that the application of our proposed methodology lets to achieve better results than the average of the market. The theme of the Master’s degree paper is “Analysis and Optimization of Swarm Intelligent in Financial Markets”. The object of the Master’s degree paper is decision making method for financial markets, re neural network and swarm intelligence.

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This thesis presents the innovative design of intelligent agent architectures specifically focusing on their human-agent teaming ability for use in a simulation environment. Agent teaming has gained popularity in recent years. It is believed that three important aspects, communication, co-ordination and co-operation play important roles in agent teaming. Multi agent teaming takes inspiration from human organisational models of team operation, where role playing such as leadership and communicative, co-operative and collaborative skills empower the success of the team. Additionally, the second major step in agent teaming is the “human-centric” nature of the agent. The current trend of agent development is very much concentrated on its agent only interaction within teams. This thesis contributes to the understanding of human-agent teaming within current agent architectures such as Belief Desire Intention (BDI) architectures. / Thesis (PhDComputerSystemsEng)--University of South Australia, 2006.

Thesis (MProjMgmt)--University of South Australia, 1998

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