This Blue Sky presentation focuses on a major shift toward a notion of “ambient intelligence” that transcends general applications targeted at the general population. The focus is on highly personalized agents that accommodate individual differences and changes over time. This notion of Extended Ambient Intelligence (EAI) concerns adaptation to a person’s preferences and experiences, as well as changing capabilities, most notably in an environment where conversational engagement is central. An important step in moving this research forward is the accommodation of different degrees of cognitive capability (including speech processing) that may vary over time for a given user—whether through improvement or through deterioration. We suggest that the application of divergence detection to speech patterns may enable adaptation to a speaker’s increasing or decreasing level of speech impairment over time. Taking an adaptive approach toward technology development in this arena may be a first step toward empowering those with special needs so that they may live with a high quality of life. It also represents an important step toward a notion of ambient intelligence that is personalized beyond what can be achieved by mass-produced, one-size-fits-all software currently in use on mobile devices.

A human-centered approach to computer systems design involves reframing analysis in terms of the people interacting with each other. The primary concern is not how people can interact with computers, but how work systems (facilities, tools, roles, and procedures) can be designed to help people pursue their personal projects, as they work independently and collaboratively. Two case studies provide empirical requirements. First, an analysis of astronaut interactions with CapCom on Earth during one traverse of Apollo 17 shows what kind of information was conveyed and what might be automated today. A variety of agent and robotic technologies are proposed that deal with recurrent problems in communication and coordination during the analyzed traverse. Second, an analysis of biologists and a geologist working at Haughton Crater in the High Canadian Arctic reveals how work interactions between people involve independent personal projects, sensitively coordinated for mutual benefit. In both cases, an agent or robotic system's role would be to assist people, rather than collaborating, because today's computer systems lack the identity and purpose that consciousness provides.

Knowledge acquisition is a process of developing qualitative models of systems in the world—physical, social, technological—often for the first time, not extracting facts and rules that are already written down and filed away in an expert's mind. Models of reasoning describe how people behave—how they interactively gather evidence by looking and asking questions, represent a situation by saying and writing things, and plan to act in some environment. But such models are inherently brittle mechanisms: Human reinterpretation of rules and procedures is metaphorical, based on prelinguistic perceptual categorization and nondeliberated sensory-motor coordination. This view of people, relative to computer models, yields an alternative view of what tools can by and the tool design process. Knowledge engineers are called to participate with social scientists and workers in the codesign of the workplace and tools for enhancing worker creativity and response to unanticipated situations. the emphasis is on augmenting human capabilities as they interact with each other to construct new conceptualizations—facilitating conversations, not just automating routine behavior. Software development in the context of use maintains connection to nontechnical, social factors such as ownership of ideas and authority to participate. the role of knowledge engineering is not merely “capturing knowledge” in a program delivered by technicians to users. Rather, we seek to develop tools that help people in a community in their everyday practice of creating new understandings and capabilities, new forms of knowledge. © 1993 John Wiley & Sons, Inc.

The Institute for Research on Learning (IRL) is a non-profit organization founded in 1986 in Palo Alto, California, committed to understanding what leads to successful learning in the schools, the workplace, and everyday life. A basic premise of IRL research, that people learn best when they are engaged with others, leads IRL's researchers to perceive schools and workplaces as communities of learners and to focus on the design of environments, technology, and activities that support learning as a collaborative activity. IRL pursues its research in collaboration with schools, universities, corporations, and government agencies—in the actual settings in which learning takes place.

Abstract An explicit representation of the problem solving method of an expert system shell as abstract control knowledge provides a powerful foundation for learning. This paper describes the abstract control knowledge of the HERACLES expert system shell for heuristic classification problems, and describes how the ODYSSEUS apprenticeship learning program uses this representation to semi-automate “endgame” knowledge acquisition. The problem solving method of HERACLES is represented explicitly as domain-independent tasks and metarules. Metarules locate and apply domain knowledge to achieve problem solving subgoals, such as testing, refining, or differentiating between hypothesis; and asking general or clarifying questions. We show how monitoring abstract control knowledge for metarule premise failures provides a means of detecting gaps in the knowledge base. A knowledge base gap will almost always cause a metarule premise failure. We also show how abstract control knowledge plays a crucial role in using underlying domain theories for learning, especially weak domain theories. The construction of abstract control knowledge requires that the different types of knowledge that enter into problem solving be represented in different knowledge relations. This provides a foundation for the integration of underlying domain theories into a learning system, because justification of different types of new knowledge usually requires different ways of using an underlying domain theory. We advocate the construction of a definitional constraint for each knowledge relation that specifies how the relation is defined and justified in terms of underlying domain theories.