Разработка многоагентной технологии управления контекстом в открытой информационной среде

Methodology and research prototype of multiagent technology for context management in an open information environment for intelligent decision support have been developed. Formalism of object-oriented constraint networks used for knowledge representation is presented. A set of technological and problem-oriented agents used for accomplishing purposes of context management in open information environment is defined. Models and scenarios of agent interactions are developed. The prototype is tested using a case study for a complex task of portable hospital configuration in an emergency situation of a disaster event.Разработаны, методология и исследовательский прототип многоагентной технологии управления контекстом в открытой информационной среде в рамках интеллектуальной поддержки принятия решений. Показано использование формализма объектно-ориентированных сетей ограничений для представления знаний. Определен набор технологических и проблемно-ориентированных агентов для решения задач управления контекстомв открытой информационной среде, разработаны модели и сценарии их взаимодействия. Исследовательский прототип протестирован на примере комплексной задачи конфигурирования мобильного госпиталя в ситуации техногенной катастрофы

Модели контекстно-управляемых систем поддержки принятия решений в динамических структурированных областях

Some existing approaches to representation and organization of contexts in different information systems are analyzed. A two-level context management model for intelligent decision support in dynamic structured domains is proposed. A model for description of information resources of an open information environment is given. A technology model of context-aware decision support system is designed.Анализируются известные подходы к описанию и формированию контекста в различных информационных средах. Предлагается двухуровневая модель управления контекстом для организации интеллектуальной поддержки принятия решений в динамических структурированных областях. Приводится модель описания ресурсов открытой информационной среды для моделирования текущей ситуации. Определена технологическая модель контекстно-управляемой системы интеллектуальной поддержки принятия решений

Leveraging lessons learned in organizations through implementing practice-based organizational learning and performance improvement - An opportunity for context-based intelligent assistant support (CIAS)

Organizations that leverage lessons learned from their experience in the practice of complex real-world activities are faced with five difficult problems. First, how to represent the learning situation in a recognizable way. Second, how to represent what was actually done in terms of repeatable actions. Third, how to assess performance taking account of the particular circumstances. Fourth, how to abstract lessons learned that are re-usable on future occasions. Fifth, how to determine whether to pursue practice maturity or strategic relevance of activities. Here, organizational learning and performance improvement are investigated in a field study using the Context-based Intelligent Assistant Support (CIAS) approach. A new conceptual framework for practice-based organizational learning and performance improvement is presented that supports researchers and practitioners address the problems evoked and contributes to a practice-based approach to activity management. The novelty of the research lies in the simultaneous study of the different levels involved in the activity. Route selection in light rail infrastructure projects involves practices at both the strategic and operational levels; it is part managerial/political and part engineering. Aspectual comparison of practices represented in Contextual Graphs constitutes a new approach to the selection of Key Performance Indicators (KPIs). This approach is free from causality assumptions and forms the basis of a new approach to practice-based organizational learning and performance improvement. The evolution of practices in contextual graphs is shown to be an objective and measurable expression of organizational learning. This diachronic representation is interpreted using a practice-based organizational learning novelty typology. This dissertation shows how lessons learned when effectively leveraged by an organization lead to practice maturity. The practice maturity level of an activity in combination with an assessment of an activity’s strategic relevance can be used by management to prioritize improvement effort

The influence of mental representations on eye movement patterns under uncertainty

This thesis investigated eye movements (i.e. number of fixations, fixation duration) during learning in uncertain situations, i.e. when interacting with a technical system like a ticket machine and users are not aware of the functioning. It was predicted that eye movements allow insights into the process of developing a mental representation under uncertainty. In order to induce uncertainty, a visual spatial search task with likely and unlikely target locations was developed. Participants were asked to predict the appearance of stimuli at target locations as accurately as possible by learning the underlying probability concept. In quick succession, they were asked to react as quickly as possible on changes of the stimuli. In total, five eye tracking experiments were gradually developed and conducted. In a first experiment, participants performed the newly developed visual spatial search task und learned the underlying probability concept of likely and unlikely target locations accurately. Eye movements became more focused, i.e. number of fixations as well as fixation duration decreased significantly over the time course of the task with increasing learning and reduced uncertainty. The aim of the second experiment was to assess to what extent search difficulty affects the development of the mental representation. Therefore, target objects were presented at an unstructured white-gray patterned background. Results showed an overall higher number of fixations than in the first experiment, however, participants also developed an accurate mental representation of the probability concept. A third experiment was designed as a relearning experiment to study the effect of initial knowledge on the development of mental representations and thus on eye movements. Participants initially learned a probability concept and in a second phase learned a different concept of target-location associations. Thereby, eye movements indicated different phases of relearning. In a fourth experiment the prediction and the reaction task were assessed separately to elucidate which dominated the development of mental representation. Results indicated that the developed mental representation of the visual spatial search task was mainly based on the prediction of the target stimuli and not on the reaction on changes of the target stimuli. In a last experiment, the manipulation of the degree of objective uncertainty by varying the probabilities of the probability concept did not lead to different eye movements. It seemed that the degree of subjective uncertainty was not affected by varying the probabilities. In conclusion, the results of the thesis demonstrated that eye movements actually gave insights into the development of mental representations under uncertainty. Eye movements informed about the learning stage, viz. the accumulation of information, independent of the content as well as the subjective uncertainty of the participants, viz. the usage of decision strategies and strategies to cope with uncertainty

Contextualizing Observational Data For Modeling Human Performance

This research focuses on the ability to contextualize observed human behaviors in efforts to automate the process of tactical human performance modeling through learning from observations. This effort to contextualize human behavior is aimed at minimizing the role and involvement of the knowledge engineers required in building intelligent Context-based Reasoning (CxBR) agents. More specifically, the goal is to automatically discover the context in which a human actor is situated when performing a mission to facilitate the learning of such CxBR models. This research is derived from the contextualization problem left behind in Fernlund\u27s research on using the Genetic Context Learner (GenCL) to model CxBR agents from observed human performance [Fernlund, 2004]. To accomplish the process of context discovery, this research proposes two contextualization algorithms: Contextualized Fuzzy ART (CFA) and Context Partitioning and Clustering (COPAC). The former is a more naive approach utilizing the well known Fuzzy ART strategy while the latter is a robust algorithm developed on the principles of CxBR. Using Fernlund\u27s original five drivers, the CFA and COPAC algorithms were tested and evaluated on their ability to effectively contextualize each driver\u27s individualized set of behaviors into well-formed and meaningful context bases as well as generating high-fidelity agents through the integration with Fernlund\u27s GenCL algorithm. The resultant set of agents was able to capture and generalized each driver\u27s individualized behaviors

A Reinforcement Learning Technique For Enhancing Human Behavior Models In A Context-based Architecture

A reinforcement-learning technique for enhancing human behavior models in a context-based learning architecture is presented. Prior to the introduction of this technique, human models built and developed in a Context-Based reasoning framework lacked learning capabilities. As such, their performance and quality of behavior was always limited by what the subject matter expert whose knowledge is modeled was able to articulate or demonstrate. Results from experiments performed show that subject matter experts are prone to making errors and at times they lack information on situations that are inherently necessary for the human models to behave appropriately and optimally in those situations. The benefits of the technique presented is two fold; 1) It shows how human models built in a context-based framework can be modified to correctly reflect the knowledge learnt in a simulator; and 2) It presents a way for subject matter experts to verify and validate the knowledge they share. The results obtained from this research show that behavior models built in a context-based framework can be enhanced by learning and reflecting the constraints in the environment. From the results obtained, it was shown that after the models are enhanced, the agents performed better based on the metrics evaluated. Furthermore, after learning, the agent was shown to recognize unknown situations and behave appropriately in previously unknown situations. The overall performance and quality of behavior of the agent improved significantly

Context Proceduralization in Decision Making

Although it seems obvious that decision making is a contextual task, papers dealing with decision making tackle rarely the problem of contextual information management. After a brief presentation of our view on context, we examine the contextual dimension of decision making. Then we explain our views about the acquisition of contextual data and the construction of a reasoning framework appropriate for decision making. We call this process proceduralization and we refer to a rational construction for action (rca)