To support the goal of allowing users to record and retrieve information, this paper describes an interactive note-taking system for pen-based computers with two distinctive features. First, it actively predicts what the user is going to write. Second, it automatically constructs a custom, button-box user interface on request. The system is an example of a learning-apprentice software- agent. A machine learning component characterizes the syntax and semantics of the user's information. A performance system uses this learned information to generate completion strings and construct a user interface. Description of Online Appendix: People like to record information. Doing this on paper is initially efficient, but lacks flexibility. Recording information on a computer is less efficient but more powerful. In our new note taking softwre, the user records information directly on a computer. Behind the interface, an agent acts for the user. To help, it provides defaults and constructs a custom user interface. The demonstration is a QuickTime movie of the note taking agent in action. The file is a binhexed self-extracting archive. Macintosh utilities for binhex are available from mac.archive.umich.edu. QuickTime is available from ftp.apple.com in the dts/mac/sys.soft/quicktime.

My Ph.D. dissertation describes PAGODA (probabilistic autonomous goal-directed agent), a model for an intelligent agent that learns autonomously in domains containing uncertainty. The ultimate goal of this line of research is to develop intelligent problem-solving and planning systems that operate in complex domains, largely function autonomously, use whatever knowledge is available to them, and learn from their experience. PAGODA was motivated by two specific requirements: The agent should be capable of operating with minimal intervention from humans, and it should be able to cope with uncertainty (which can be the result of inaccurate sensors, a nondeterministic environment, complexity, or sensory limitations). I argue that the principles of probability theory and decision theory can be used to build rational agents that satisfy these requirements.

My Ph.D. dissertation describes PAGODA (probabilistic autonomous goal-directed agent), a model for an intelligent agent that learns autonomously in domains containing uncertainty. The ultimate goal of this line of research is to develop intelligent problem-solving and planning systems that operate in complex domains, largely function autonomously, use whatever knowledge is available to them, and learn from their experience. PAGODA was motivated by two specific requirements: The agent should be capable of operating with minimal intervention from humans, and it should be able to cope with uncertainty (which can be the result of inaccurate sensors, a nondeterministic environment, complexity, or sensory limitations). I argue that the principles of probability theory and decision theory can be used to build rational agents that satisfy these requirements.

Recent attempts to develop larger and more complex knowledge-based systems have revealed the shortcomings and problems of centralized, single-agent architectures and have acted as a springboard for research in distributed AI (DAI). Although initial research efforts in DAI concentrated on issues relating to homogeneous systems (that is, systems using agents of a similar type or with similar knowledge), there is now increasing interest in systems comprised of heterogeneous components. The workshop on cooperation among heterogeneous intelligent agents, held July 15 during the 1991 National Conference on Artificial Intelligence, was organized by Evangelos Simoudis, Mark Adler, Michael Huhns, and Edmund Durfee. It was designed to bring together researchers and practitioners who are studying how to enable a heterogeneous collection of independent intelligent systems to cooperate in solving problems that require their combined abilities.

We summarize the Among the workshop's principal The in using these systems, and (6) computer represent the same knowledge differently workshop on cooperation among environments that facilitate to optimize their particular use heterogeneous intelligent agents, cooperation among human problem of it, or agents could obtain knowledge held July 15 during the 1991 National solvers of diverse abilities. DAI system can use as agents a collection and Edmund Durfee. It was designed Fifty submissions were received, and of existing knowledge-based to bring together researchers and 43 contributors were invited to the systems that have been developed practitioners who are studying how workshop. The workshop had four under a variety of implementation to enable a heterogeneous collection sessions that covered the topics of philosophies. In particular, representations create a special type of agent that is Fifth, agents negotiate and converge must be agreed on able to act as a broker to each of the on decisions by making deals (either before invocation or as a existing agents that need to participate under various types of pressure. Methods must also in a blackboard architecture, so it can be created for agents to assimilate cooperate with other agents.

The Association for the Advancement of Artificial Intelligence held its 1991 Fall Symposium Series on November 15-17 at the Asilomar Conference Center, Pacific Grove, California. This article contains summaries of the four symposia: Discourse Structure in Natural Language Understanding and Generation, Knowledge and Action at Social and Organizational Levels, Principles of Hybrid Reasoning, Sensory Aspects of Robotic Intelligence.

The title of this paper, "On Seeing Robots", leaves substantial scope for playful exploration. The simple ambiguity is, of course, between describing robots that see their worlds and systems that see robots. These categories are not exclusive: I also combine them and discuss robots that see robots and even robots that see themselves. Furthermore, the title is designed to echo, and pay homage to, a classic vision paper entitled "On Seeing Things" by Max Clowes [1] as I have done once before [2]. But the context, the arguments and the conclusions are new; the comparison is used explicitly here to show the difference between the classical approach and an emerging situated approach to robotic perception. The most important reading of the title is that the paper is about how we see robots; it is about the computational paradigms, the assumptions, the architectures and the tools we use to design and build robots.

This article argues that autonomy, not problem-solving prowess, is the key property that defines the intuitive notion of "intelligent creature." The presence of these attributes gives autonomous systems the appearance of nondeterminism, but they can all be present in deterministic artifacts and living systems. We argue that autonomy means having the right kinds of goals and the ability to select goals from an existing set, not necessarily creating new goals. We analyze the concept of goals in problem-solving systems in general and establish criteria for the types of goals that characterize autonomy.

Each is supported and work to find a general solution to that by a formalized theory of the moves in the class of problems.

This article argues that autonomy, not problem-solving prowess, is the key property that defines the intuitive notion of "intelligent creature." To build an intelligent artificial entity that will act autonomously, we must first understand the attributes of a system that lead us to call it autonomous. The presence of these attributes gives autonomous systems the appearance of nondeterminism, but they can all be present in deterministic artifacts and living systems. We argue that autonomy means having the right kinds of goals and the ability to select goals from an existing set, not necessarily creating new goals. We analyze the concept of goals in problem-solving systems in general and establish criteria for the types of goals that characterize autonomy.