Simulating activities: Relating motives, deliberation, and attentive coordination

Activities are located behaviors, taking time, conceived as socially meaningful, and usually involving interaction with tools and the environment. In modeling human cognition as a form of problem solving (goal-directed search and operator sequencing), cognitive science researchers have not adequately studied “off-task” activities (e.g., waiting), non-intellectual motives (e.g., hunger), sustaining a goal state (e.g., playful interaction), and coupled perceptual-motor dynamics (e.g., following someone). These aspects of human behavior have been considered in bits and pieces in past research, identified as scripts, human factors, behavior settings, ensemble, flow experience, and situated action. More broadly, activity theory provides a comprehensive framework relating motives, goals, and operations. This paper ties these ideas together, using examples from work life in a Canadian High Arctic research station. The emphasis is on simulating human behavior as it naturally occurs, such that “working” is understood as an aspect of living. The result is a synthesis of previously unrelated analytic perspectives and a broader appreciation of the nature of human cognition. Simulating activities in this comprehensive way is useful for understanding work practice, promoting learning, and designing better tools, including human-robot systems

A comparison of languages which operationalise and formalise {KADS} models of expertise

In the field of Knowledge Engineering, dissatisfaction with the rapid-prototyping approach has led to a number of more principled methodologies for the construction of knowledge-based systems. Instead of immediately implementing the gathered and interpreted knowledge in a given implementation formalism according to the rapid-prototyping approach, many such methodologies centre around the notion of a conceptual model: an abstract, implementation independent description of the relevant problem solving expertise. A conceptual model should describe the task which is solved by the system and the knowledge which is required by it. Although such conceptual models have often been formulated in an informal way, recent years have seen the advent of formal and operational languages to describe such conceptual models more precisely, and operationally as a means for model evaluation. In this paper, we study a number of such formal and operational languages for specifying conceptual models. In order to enable a meaningful comparison of such languages, we focus on languages which are all aimed at the same underlying conceptual model, namely that from the KADS method for building KBS. We describe eight formal languages for KADS models of expertise, and compare these languages with respect to their modelling primitives, their semantics, their implementations and their applications. Future research issues in the area of formal and operational specification languages for KBS are identified as the result of studying these languages. The paper also contains an extensive bibliography of research in this area

Technology Directions for the 21st Century

The Office of Space Communications (OSC) is tasked by NASA to conduct a planning process to meet NASA's science mission and other communications and data processing requirements. A set of technology trend studies was undertaken by Science Applications International Corporation (SAIC) for OSC to identify quantitative data that can be used to predict performance of electronic equipment in the future to assist in the planning process. Only commercially available, off-the-shelf technology was included. For each technology area considered, the current state of the technology is discussed, future applications that could benefit from use of the technology are identified, and likely future developments of the technology are described. The impact of each technology area on NASA operations is presented together with a discussion of the feasibility and risk associated with its development. An approximate timeline is given for the next 15 to 25 years to indicate the anticipated evolution of capabilities within each of the technology areas considered. This volume contains four chapters: one each on technology trends for database systems, computer software, neural and fuzzy systems, and artificial intelligence. The principal study results are summarized at the beginning of each chapter

Ontology based data warehousing for mining of heterogeneous and multidimensional data sources

Heterogeneous and multidimensional big-data sources are virtually prevalent in all business environments. System and data analysts are unable to fast-track and access big-data sources. A robust and versatile data warehousing system is developed, integrating domain ontologies from multidimensional data sources. For example, petroleum digital ecosystems and digital oil field solutions, derived from big-data petroleum (information) systems, are in increasing demand in multibillion dollar resource businesses worldwide. This work is recognized by Industrial Electronic Society of IEEE and appeared in more than 50 international conference proceedings and journals

Mind as Machine: Can Computational Processes Be Regarded As Explanatory of Mental Processes?

The aim of the thesis is to evaluate recent work in artificial intelligence (AI). It is argued that such evaluation can be philosophically interesting, and examples are given of areas of the philosophy of AI where insufficient concentration on the actual results of AI has led to missed opportunities for the two disciplines — philosophy and AI — to benefit from cross-fertilization. The particular topic of the thesis is the use of AI techniques in psychological explanation. The claim is that such techniques can be of value in psychology, and the strategy of proof is to exhibit an area where this is the case. The field of model-based knowledge-based system (KBS) development is outlined; a type of model called a conceptual model will be shown to be psychologically explanatory of the expertise that it models. A group of major philosophies of explanation are examined, and it is discovered that such philosophies are too restrictive and prescriptive to be of much value in evaluating many areas of science; they fail to apply to scientific explanation generally. The importance of having sympathetic yardsticks for the evaluation of explanatory practices in arbitrary fields is defended, and a series of such yardsticks is suggested. The practice of providing information processing models in psychology is discussed. A particular type of model, a psychological competence model, is defined, and its use in psychological explanation defended. It is then shown that conceptual models used in model-based KBS development are psychological competence models. It follows therefore that such models are explanatory of the expertise they model. Furthermore, since KBSs developed using conceptual models share many structural characteristics with their conceptual models, it follows that a limited class of those systems are also explanatory of expertise. This constitutes an existence proof that computational processes can be explanatory of mental processes

A Generic Library of Problem Solving Methods for Scheduling Applications

In this thesis we propose a generic library of scheduling problem-solving methods. As a first approximation, scheduling can be defined as an assignment of jobs and activities to resources and time ranges in accordance with a number of constraints and requirements. In some cases optimisation criteria may also be included in the problem specification. Although, several attempts have been made in the past at developing the libraries of scheduling problem-solvers, these only provide limited coverage. Many lack generality, as they subscribe to a particular scheduling domain. Others simply implement a particular problem-solving technique, which may be applicable only to a subset of the space of scheduling problems. In addition, most of these libraries fail to provide the required degree of depth and precision, which is needed both to obtain a formal account of scheduling problem solving and to provide effective support for development of scheduling applications by reuse. Our library subscribes to the Task-Method-Domain-Application (TMDA) knowledge modelling framework, which provides a structured organisation for the different components of the library. In line with the organisation proposed by TMDA, we first developed a generic scheduling task ontology, which formalises the space of scheduling problems independently of any particular application domain, or problem solving method. Then we constructed a task-specific, but domain independent model of scheduling problem-solving, which generalises from the variety of approaches to scheduling problem-solving, which can be found in literature. The generic nature of this model was demonstrated by constructing seven methods for scheduling, as alternative specialisation of the model. Finally, we validated our library on a number of applications to demonstrate its generic nature and effective support for the analysis and development of scheduling applications