Many powerful Artificial Intelligence (AI) techniques have been engineered with the goals of high performance and accuracy. Recently, AI algorithms have been integrated into diverse and real-world applications. It has become an important topic to explore the impact of AI on society from a people-centered perspective. Previous works in citizen science have identified methods of using AI to engage the public in research, such as sustaining participation, verifying data quality, classifying and labeling objects, predicting user interests, and explaining data patterns. These works investigated the challenges regarding how scientists design AI systems for citizens to participate in research projects at a large geographic scale in a generalizable way, such as building applications for citizens globally to participate in completing tasks. In contrast, we are interested in another area that receives significantly less attention: how scientists co-design AI systems "with" local communities to influence a particular geographical region, such as community-based participatory projects. Specifically, this article discusses the challenges of applying AI in Community Citizen Science, a framework to create social impact through community empowerment at an intensely place-based local scale. We provide insights in this under-explored area of focus to connect scientific research closely to social issues and citizen needs.

We present a new robot platform, the Finch, that was designed to align with the learning goals and concepts taught in introductory computer science courses. The Finch was developed in the context of the CSbots program, the goal of which is to improve retention and learning in computer science courses through the use of robots and other physically embodied hardware. This paper concentrates on design constraints that were determined in earlier CSbots studies and how those constraints were instantiated by the Finch. We also present some preliminary results from pilot studies in which Finch robots were used in CS1 and CS2 classes.

The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University's Computer Science Department, was pleased to present its 2006 Spring Symposium Series held March 27-29, 2006, at Stanford University, California.

The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University's Computer Science Department, was pleased to present its 2006 Spring Symposium Series held March 27-29, 2006, at Stanford University, California. The titles of the eight symposia were (1) Argumentation for Consumers of Health Care (chaired by Nancy Green); (2) Between a Rock and a Hard Place: Cognitive Science Principles Meet AI Hard Problems (chaired by Christian Lebiere); (3) Computational Approaches to Analyzing Weblogs (chaired by Nicolas Nicolov); (4) Distributed Plan and Schedule Management (chaired by Ed Durfee); (5) Formalizing and Compiling Background Knowledge and Its Applications to Knowledge Representation and Question Answering (chaired by Chitta Baral); (6) Semantic Web Meets e-Government (chaired by Ljiljana Stojanovic); (7) To Boldly Go Where No Human-Robot Team Has Gone Before (chaired by Terry Fong); and (8) What Went Wrong and Why: Lessons from AI Research and Applications (chaired by Dan Shapiro).

The staff scheduling problem is a critical problem in the call center (or, more generally, customer contact center) industry. This article describes DIRECTOR, a staff scheduling system for contact centers. DIRECTOR is a constraint-based system that uses AI search techniques to generate schedules that satisfy and optimize a wide range of constraints and service-quality metrics. DIRECTOR has successfully been deployed at more than 800 contact centers, with significant measurable benefits, some of which are documented in case studies included in this article.

The staff scheduling problem is a critical problem in the call center (or, more generally, customer contact center) industry. This article describes DIRECTOR, a staff scheduling system for contact centers. DIRECTOR is a constraint-based system that uses AI search techniques to generate schedules that satisfy and optimize a wide range of constraints and service-quality metrics. DIRECTOR has successfully been deployed at more than 800 contact centers, with significant measurable benefits, some of which are documented in case studies included in this article.

The Fifth Annual AAAI Mobile Robot Competition and Exhibition was held in Portland, Oregon, in conjunction with the Thirteenth National Conference on Artificial Intelligence. The first event stressed navigation and planning. In addition to the competition, there was a mobile robot exhibition in which teams demonstrated robot behaviors that did not fit into the competition tasks. The robot competition raised the standard for autonomous mobile robotics, demonstrating the intelligent integration of perception, deliberation, and action.

The Fifth Annual AAAI Mobile Robot Competition and Exhibition was held in Portland, Oregon, in conjunction with the Thirteenth National Conference on Artificial Intelligence. The competition consisted of two events: (1) Office Navigation and (2) Clean Up the Tennis Court. The first event stressed navigation and planning. The second event stressed vision sensing and manipulation. In addition to the competition, there was a mobile robot exhibition in which teams demonstrated robot behaviors that did not fit into the competition tasks. The competition and exhibition were unqualified successes, with nearly 20 teams competing. The robot competition raised the standard for autonomous mobile robotics, demonstrating the intelligent integration of perception, deliberation, and action.

DERVISH won the Office Delivery event of the 1994 Robot Competition and Exhibition, held as part of the Thirteenth National Conferennce on Artificial Intelligence. Although the contest required dervish to navigate in an artificial office environment, the official goal of the contest was to push the technology of robot navigation in real office buildings with minimal domain information. In this article, we present a short description of Dervish's hardware and low-level motion modules. We then discuss this assumptive system in more detail.

DERVISH won the Office Delivery event of the 1994 Robot Competition and Exhibition, held as part of the Thirteenth National Conferennce on Artificial Intelligence. Although the contest required dervish to navigate in an artificial office environment, the official goal of the contest was to push the technology of robot navigation in real office buildings with minimal domain information. dervish navigates reliably using retractable assumptions that simplify the planning problem. In this article, we present a short description of Dervish's hardware and low-level motion modules. We then discuss this assumptive system in more detail.