Each political decision in fact implies some form of social reactions, it affects economic and financial aspects and has substantial environmental impacts. Improving decision making in this context could have a huge beneficial impact on all these aspects. There are a number of Artificial Intelligence techniques that could play an important role in improving the policy-making process such as decision support and optimization techniques, game theory, data and opinion mining and agent-based simulation. We outline here some potential use of AI technology as it emerged by the European Union (EU) EU FP7 project ePolicy: Engineering the Policy Making Life Cycle, and we identify some potential research challenges. They are extremely complex, occur in rapidly changing environments characterized by uncertainty, and involve conflicts among different interests.

The titles of the seven symposia were Artificial Intelligence for Human-Robot Interaction; Energy Market Prediction; Expanding the Boundaries of Health Informatics Using AI; Knowledge, Skill, and Behavior Transfer in Autonomous Robots; Modeling Changing Perspectives: Reconceptualizing Sensorimotor Experiences; Natural Language Access to Big Data; and The Nature of Humans and Machines: A Multidisciplinary Discourse. The highlights of each symposium are presented in this report. The primary goal of the AI for Human-Robot Interaction symposium was to bring together and strengthen the community of researchers working on the AI challenges inherent to human-robot interaction (HRI). HRI is an extremely interesting problem domain for AI and robotics research. It aims to develop robots that are intelligent, autonomous, and capable of interacting with, modeling, and learning from humans.

A hybrid fuzzy knowledge-based system with crisp and fuzzy rules as well as numerical methods was developed for multiobjective optimization of power distribution system operation. The development process and knowledge-acquisition process for the fuzzy knowledge-based system are described in detail. Fuzzy sets are defined for recent temperature trend, line section loading, transformer aging, voltage-level guidelines, and the degree of desirability of a proposed switching combination. After a heuristic preprocessor proposes a list of switch openings that would seem to reduce system losses, network radiality rules consider whether to open a particular switch and find a corresponding switch that can be closed to maintain radiality. Network parameter rules determine whether the proposed switching combination will violate network integrity.

The fall of 2002 marked the passing of Ray Reiter, for whom a memorial article by Jack Minker appears in this issue. As the issue was going to press, AI lost Saul Amarel, Norm Nielsen, and Charles Rosen. We thank Tom Mitchell and Casimir Kulikowski for their memorial to Saul Amarel, Ray Perrault for his remembrance of Norm Nielsen, and Peter Hart and Nils Nilsson for their tribute to Charles Rosen. The AI community mourns our lost colleagues and gratefully remembers their contributions, which meant so much to so many and to the advancement of artificial intelligence as a whole. The foundation of Charlie's creativity was his broad knowledge.

"With autonomy we declare that no sphere is off limits. We will send our spacecraft to search beyond the horizon, accepting that we cannot directly control them, and relying on them to tell the tale." A new generation of sensor-rich, massively distributed, autonomous systems are being developed that have the potential for profound social, environmental, and economic change. 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. To achieve high performance, these immobile robots (or immobots) will need to develop sophisticated regulatory and immune systems that accurately and robustly control their complex internal functions.

I highlight several of these applications, using a simple energy storage problem as a case application. Using this setting, I describe a modeling framework that is based on five fundamental dimensions and that is more natural than the standard canonical form widely used in the reinforcement learning community. The framework focuses on finding the best policy, where I identify four fundamental classes of policies consisting of policy function approximations (PFAs), cost function approximations (CFAs), policies based on value function approximations (VFAs), and look-ahead policies. There is the familiar array of decisions: discrete actions, continuous controls, and vector-valued (and possibly integer) decisions. The tools for these problems are drawn from computer science, engineering, applied math, and operations research.

In this issue of AI Magazine, we continue our presentation of extended versions of papers presented at IAAI-12 (held in Toronto, Ontario, Canada) that were selected for their description of AI technologies that are in practical use. Our selections for this issue describe deployed applications. They explain the context, requirements, and constraints of the application, how the technology was adapted to satisfy those factors, and the impact that this innovation brought to the operation in terms of cost and performance. The articles also supply useful insights into use cases that we hope can also be translated to other work that the AI community is engaged in. In the first of these deployed application articles, eBird: A Human/Computer Learning Network to Improve Biodiversity Conservation and Research by Steve Kelling, Carl Lagoze, Weng-Keen Wong, Jun Yu, Theodoros Damoulas, Jeff Gerbracht, Daniel Fink, and Carla Gomes, the authors describe an intriguing application that successfully combines the best in human and artificial computing capabilities with an active feedback loop between people and machines.

AI activities are also being pursued at other Schlumberger locations, often jointly with SDR The locations related to logging and interpretation include: Schlumberger-Doll Research, Ridgefield, Connecticut (Contact: Peter Wu'l); Schlumberger Well Services, Austin, Texas (Contact: Scott Gut/my); Schlumberger Well Services, Houston, Texas (Contact: Scott Ma&s); Nippon Schlumberger, K K, Tokyo, Japan (Contact: Dennzs O'NezU); I&ude et Production Schlumbcraer. Other Schlumberger companies involied in Ai research include! Expert Systems Current work in expert, systems is concerned with developing techniques for building more robust and versatile log interpretation systems. One shortcoming of "first generation" expert systems, such as the Dipmeter Advisor, is their inability to reason about the task that they attempt to perform. Any description of the overall task is usually procedurally encoded and unavailable for examination.

Shell U.K. Exploration and Production (Aberdeen, U.K.) has implemented an advanced forecasting system for predicting oil field production. The expert system helped Shell achieve over $1.6 million in cost savings for its Brent Field site within 2 months of implementation. The National Research Council has awarded Nestor (Providence, R.I.) a grant to develop a neural network-based video sensor system, crossingguard Arvin Industries (Columbus, Ind.) is working with the U.S. Air Force to develop a neural network system that can determine the quality of noise in such vehicles as automobiles and aircraft. The neural network will help determine what exactly an annoying sound is and how it can be fixed. Using virtual reality hardware and software, Parke-Davis (Morris Plains, N.J.) has been able to improve the molecular modeling research techniques it uses to develop new pharmaceutical products.

Microelectronics supplier TRW (Redondo Beach, CA) is using virtual reality (VR) to decontaminate nuclear facilities. Clothing manufacturer Wrangler (Greensboro, NC) has developed a neural network system to improve production planning and forecasting. The system generates forecasts based on consumer-demand data, rather than retail buyers' orders, to drive production planning. Unisys Corp. (Blue Bell, PA), an information-management company, is using an intelligent agent-based system to provide self-service humanresource applications to its 36,000 employees worldwide. The intelligent agents allow employees secure and controlled access to their records through the World Wide Web.