In this article I discuss a deployed application at Ford Motor Company that utilizes AI technology for the analysis of potential ergonomic concerns at Ford's assembly plants. The manufacture of motor vehicles is a complex and dynamic problem, and the costs related to workplace injuries and lost productivity due to bad ergonomic design can be very significant. Ford has developed two separate ergonomic analysis systems that have been integrated into the process planning for manufacturing system at Ford known as the Global Study and Process Allocation System (GSPAS). GSPAS has become the global repository for standardized engineering processes and data for assembling all Ford vehicles, including parts, tools, and standard labor time.

In recent years, we have witnessed the success of autonomous agents applying machine-learning techniques across a wide range of applications. However, agents applying the same machine-learning techniques in online applications have not been so successful. Even agent-based hybrid recommender systems that combine information filtering techniques with collaborative filtering techniques have been applied with considerable success only to simple consumer goods such as movies, books, clothing, and food. Yet complex, adaptive autonomous agent systems that can handle complex goods such as real estate, vacation plans, insurance, mutual funds, and mortgages have emerged.

This article describes requirements for synthetic adversaries for urban combat training and a prototype application, MOUTBots. MOUTBots use a commercial computer game to define, implement, and test basic behavior representation requirements and the Soar architecture as the engine for knowledge representation and execution. The article describes how these components aided the development of the prototype and presents an initial evaluation against competence, taskability, fidelity, variability, transparency, and efficiency requirements.

Since 1994, GE Plastics has employed a case-based reasoning (CBR) tool that determines color formulas that match requested colors. This tool, called FormTool, has saved GE millions of dollars in productivity and material (that is, colorant) costs. The technology developed in FormTool has been used to create an online color-selection tool for our customers called ColorXpress Select. A customer innovation center has been developed around the FormTool software.

TheGeneral Motors Variation-Reduction Adviser is a knowledge system built on case-based reasoning principles that is currently in use in eighteen General Motors asssembly centers. This article reviews the overall characteristics of the system and then focuses on various AI elements critical to support its deployment to a production system. A key AI enabler is ontology-guided search using domainspecific ontologies.

We describe the application of plan-recognition techniques to support human intelligence analysts in processing national security alerts. Identifying intent enables us to both prioritize and explain alert sets to analysts in a readily digestible format. Our empirical evaluation demonstrates that the approach can handle alert sets of as many as 20 elements and can readily distinguish between false and true alarms. We discuss the important opportunities for future work that will increase the cardinality of the alert sets to the level demanded by a deployable application.

Learning how to create, test, and revise models is a central skill in scientific reasoning. We argue that qualitative modeling provides an appropriate level of representation for helping middle-school students learn to become modelers. We describe Vmodel, a system we have created that uses visual representations and that enables middle-school students to create qualitative models. We discuss the design of the visual representation language, how Vmodel works, and evidence from school studies that indicate it is successful in helping students.

The Association for the Advancement of Artificial Intelligence presented its 2005 Spring Symposium Series on Monday through Wednesday, March 21-23, 2005 at Stanford University in Stanford, California. The topics of the eight symposia in this symposium series were (1) AI Technologies for Homeland Security; (2) Challenges to Decision Support in a Changing World; (3) Developmental Robotics; (4) Dialogical Robots: Verbal Interaction with Embodied Agents and Situated Devices; (5) Knowledge Collection from Volunteer Contributors; (6) Metacognition in Computation; (7) Persistent Assistants: Living and Working with AI; and (8) Reasoning with Mental and External Diagrams: Computational Modeling and Spatial Assistance.

The thirteenth AAAI Mobile Robot Competition and Exhibition was once again collocated with AAAI-2204, in San Jose, California. As in previous years, the robot events drew competitors from both academia and industry to showcase state-ofthe- art mobile robot software and systems in four organized events.

RoboCup is an international initiative with the main goals of fostering research and education in artificial intelligence and robotics, as well as of promoting science and technology to world citizens. The idea behind RoboCup is to provide a standard problem for which a wide range of technologies can be integrated and examined, as well as being used for project-oriented education, and to organize annual events open to the general public, at which different solutions to the problem are compared. The eighth annual RoboCup -- RoboCup 2004 -- was held in Lisbon, Portugal, from 27 June to 5 July. In this article, a general description of RoboCup 2004 is presented, including summaries concerning teams, participants, distribution into leagues, main research advances, as well as detailed descriptions for each league.