These systems include networked building energy systems, autonomous space probes, chemical plant control systems, satellite constellations for remote ecosystem monitoring, power grids, biospherelike life-support systems, and reconfigurable traffic systems, to highlight but a few. Achieving these large-scale modeling and configuration tasks will require a tight coupling between the higher-level coordination function provided by symbolic reasoning and the lower-level autonomic processes of adaptive estimation and control. To be economically viable, they will need to be programmable purely through high-level compositional models. Self-modeling and self-configuration, autonomic functions coordinated through symbolic reasoning, and compositional, model-based programming are the three key elements of a model-based autonomous system architecture that is taking us into the new millennium.

Giving robots the ability to operate in the real world has been, and continues to be, one of the most difficult tasks in AI research. Their research has been focused on using adaptive, vision-based systems to increase the driving performance of the Navlab line of on-road mobile robots. This research has led to the development of a neural network system that can learn to drive on many road types simply by watching a human teacher. This article describes the evolution of this system from a research project in machine learning to a robust driving system capable of executing tactical driving maneuvers such as lane changing and intersection navigation.

The Association for the Advancement of Artificial Intelligence held its 1996 Spring Symposia Series on March 27 to 29 at Stanford University. This article contains summaries of the eight symposia that were conducted: (1) Acquisition, Learning, and Demonstration: Automating Tasks for Users; (2) Adaptation, Coevolution, and Learning in Multiagent Systems; (3) Artificial Intelligence in Medicine: Applications of Current Technologies; (4) Cognitive and Computational Models of Spatial Representation; (5) Computational Implicature: Computational Approaches to Interpreting and Generating Conversational Implicature; (6) Computational Issues in Learning Models of Dynamic Systems; (7) Machine Learning in Information Access; and (8) Planning with Incomplete Information for Robot Problems.

From the scientific perspective, the development of theories and mechanisms to enable building collaborative systems presents exciting research challenges across AI subfields. From the applications perspective, the capability to collaborate with users and other systems is essential if large-scale information systems of the future are to assist users in finding the information they need and solving the problems they have. Key features of collaborative activity are described, the scientific base provided by recent AI research is discussed, and several of the research challenges posed by collaboration are presented. It is further argued that research on, and the development of, collaborative systems should itself be a collaborative endeavor -- within AI, across subfields of computer science, and with researchers in other fields.

The University of Chicago's robot, CHIP, is part of the Animate Agent Project, aimed at understanding the software architecture and knowledge representations needed to build a general-purpose robotic assistant. CHIP's strategy for the Office Cleanup event of the 1995 Robot Competition and Exhibition was to scan an entire area systematically and, as collectible objects were identified, pick them up and deposit them in the nearest appropriate receptacle. This article describes CHIP and its various systems and the ways in which these elements combined to produce an effective entry to the robot competition.

The 1995 Robot Competition and Exhibition was held in Montreal, Canada, in conjunction with the 1995 International Joint Conference on Artificial Intelligence. The competition was designed to demonstrate state-of-the-art autonomous mobile robots, highlighting such tasks as goal-directed navigation, feature detection, object recognition, identification, and physical manipulation as well as effective human-robot communication. The competition consisted of two separate events: (1) Office Delivery and (2) Office Cleanup. The exhibition also consisted of two events: (1) demonstrations of robotics research that was not related to the contest and (2) robotics focused on aiding people who are mobility impaired.

CAIR-2 from the Korea Advanced Institute of Science and Technology (KAIST) placed first in the Office Delivery event at the 1995 Robot Competition and Exhibition, held in conjunction with the Fourteenth International Joint Conference on Artificial Intelligence (IJCAI-95). CAIR-2 is a totally self-contained and autonomous mobile robot, and its control architecture incorporates both behavior-based and planner-based approaches. In this article, we present a short description of CAIR-2's hardware, system and control architecture, realtime vision, and speech recognizer.

The Association for the Advancement of Artificial Intelligence (AAAI) held its 1995 Fall Symposia Series on 10 to 12 November in Cambridge, Massachusetts. This article contains summaries of the eight symposia that were conducted: (1) Active Learning; (2) Adaptation of Knowledge for Reuse; (3) AI Applications in Knowledge Navigation and Retrieval; (4) Computational Models for Integrating Language and Vision; (5) Embodied Language and Action Symposium; (6) Formalizing Context; (7) Genetic Programming; and (8) Rational Agency: Concepts, Theories, Models, and Applications.

The Association for the Advancement of Artificial Intelligence (AAAI) held its 1995 Fall Symposia Series on 10 to 12 November in Cambridge, Massachusetts. This article contains summaries of the eight symposia that were conducted: (1) Active Learning; (2) Adaptation of Knowledge for Reuse; (3) AI Applications in Knowledge Navigation and Retrieval; (4) Computational Models for Integrating Language and Vision; (5) Embodied Language and Action Symposium; (6) Formalizing Context; (7) Genetic Programming; and (8) Rational Agency: Concepts, Theories, Models, and Applications.

AUTOCELL is a system developed to assist in the operation and management of cellular mobile networks operated by Singapore Telecom. Its deployment is in line with the company's strategic move to introduce intelligent software into its operations. With the help of AI concepts and techniques, the system has enhanced the operational efficiency and network capacity and increased customer satisfaction with the network.