Instead, patrol units move about the transit system, inspecting the tickets of passengers, who face fines if caught fare evading. The deterrence of fare evasion depends on the unpredictability and effectiveness of the patrols. TRUSTS models the problem of computing patrol strategies as a leader-follower Stackelberg game where the objective is to deter fare evasion and hence maximize revenue. This problem differs from previously studied Stackelberg settings in that the leader strategies must satisfy massive temporal and spatial constraints; moreover, unlike in these counterterrorism-motivated Stackelberg applications, a large fraction of the ridership might realistically consider fare evasion, and so the number of followers is potentially huge. A third key novelty in our work is deliberate simplification of leader strategies to make patrols easier to execute. We present an efficient algorithm for computing such patrol strategies and present experimental results using real-world ridership data from the Los Angeles Metro Rail system. The Los Angeles County Sheriff's Department is currently carrying out trials of TRUSTS. There are, quite literally, no barriers to entry, as illustrated in figure 1. Instead, security personnel are dynamically deployed throughout the transit system, randomly inspecting passenger tickets. This proof-of-payment fare collection method is typically chosen as a more cost-effective alternative to direct fare collection, that is, when the revenue lost to fare evasion is believed to be less than what it would cost to make fare evasion impossible. For the LA Metro, with approximately 300,000 riders daily, this revenue loss can be significant; the annual cost has been estimated at $5.6 million. The Los Angeles Sheriff's Department (LASD) deploys uniformed patrols onboard trains and at stations for fare checking (and for other purposes such as crime prevention), in order to discourage fare evasion.

McCarthy, a past president of AAAI and an AAAI Fellow, helped design the foundation of today's internet-based computing and is widely credited with coining the term, artificial intelligence. This remembrance by Edward Feigenbaum, also a past president of AAAI and a professor emeritus of computer science at Stanford University, was delivered at the celebration of John McCarthy's accomplishments, held at Stanford on 25 March 2012. Everyone knew everyone else, and saw them at the few conference panels that were held. At one of those conferences, I met John. We renewed contact upon his rearrival at Stanford, and that was to have major consequences for my professional life.

A key objective of the competition has been to analyze and search the design space of negotiating agents for agents that are able to operate effectively across a variety of domains. The competition is a valuable tool for studying important aspects of negotiation including profiles and domains, opponent learning, strategies, and bilateral and multilateral protocols. Two of the challenges that remain are how to develop argumentation-based negotiation agents that, in addition to making offers, can inform and argue to obtain an acceptable agreement for both parties; and how to create agents that can negotiate in a human fashion. Challenges lie in the complexity of the negotiation domain, in the strategies for bidding and accepting, for opponent modeling, and so on. Competitions have proved their value as useful and open benchmarking tools to evaluate and compare agents in a common setting (for example, the successful Annual Computer Poker Competition and the various Trading Agent Competitions).

Our top lead consultant had been working with them for months to integrate the software into their back-end databases and front-end call center software (no mean feat: these were Windows PCs simulating IBM "green screens"!), and tuning the recommender to produce high quality recommendations that were successful against historical sales data. The recommendations were designed to be delivered in real time to the call center agents during live inbound calls. For instance, if the customer ordered the pink housecoat, the recommender might suggest the fuzzy pink slippers to go with it, based on prior sales experience. The company was ready for a big test: our lead consultant was standing behind one of the call center agents, watching her receive calls. Then the moment came: the IT folk at the company pushed the metaphoric big red button and switched her over to the automated recommender system.

Woodrow Wilson (Woody) Bledsoe died on 4 October 1995 of ALS, more commonly known as Lou Gehrig's disease. Woody was one of the founders of AI, making early contributions in pattern recognition and automated reasoning. He continued to make significant contributions to AI throughout his long career. His legacy consists not only of his scientific work but also of several generations of scientists who learned from Woody the joy of scientific research and the way to go about it. Woody's enthusiasm, his perpetual sense of optimism, his can-do attitude, and his deep sense of duty to humanity offered those who knew him the hope and comfort that truly good and great men do exist. Woody was one of the founders of AI, making early contributions in pattern recognition and automated reasoning. He continued to make significant contributions to AI throughout his long career. His legacy consists not only of his scientific work but also of several generations of scientists who learned from Woody the ...

Ray the adventurer was always eager to try new ideas and directions. He was not afraid to enter murky areas, and he always left them better illuminated. He introduced terms to the AI community such as default logic, closed-world assumption, and cognitive robotics; he opened avenues of theoretical research with new resolution proof methods and logics for nonmonotonic reasoning, diagnosis, and action; and he was the prime mover in the Cognitive Robotics initiative that has led to a whole new program of research. And he was an adventurer in more than just ideas. He frequently traveled to remote locations to add to his extraordinary collection of rare and exotic lepidoptera.

He is survived by his wife, Dagmar Dolan, and his 15 year old daughter Bronja Prazdny. Slava was recognized internationally as an expert in many aspects of human and machine perception. During his prolific career, he had published over 60 journal articles reporting research in human perception, stereo vision, image processing, robotics, perceptual reasoning and learning, adaptive neural networks, and psychophysics. Slava derived his greatest pleasure from concocting clever demonstrations aimed at destroying currently popular theories of perception His work on transparent random-dot stereograms, for example, challenges widely-held theories of stereopsis that rely on surface continuity assumptions to resolve depth ambiguities. We were fortunate to have worked beside Slava during his tenure in California.

Robert S. (Bob) Engelmore, who retired in 1998 from the Knowledge Systems Laboratory at Stanford University, died in an ocean accident in Hawaii on March 25, 2003. As the second editor of AI Magazine, he guided its development from 1981 to 1991; he was also elected a fellow of AAAI in 1992. He had been involved in many aspects of AI and was respected for his uncommon common sense and good humor. He played football for Briarcliff Manor High School, learned to play the piano, and most importantly nurtured a deep interest in science. He won a nationally prestigious Westinghouse science scholarship to Carnegie Institute of Technology (later Carnegie Mellon University) and became a physics major.

Raymond Reiter, a professor of computer science at the University of Toronto, a fellow of the Royal Society of Canada, and winner of the International Joint Conference on Artificial Intelligence 1993 Outstanding Research Scientist Award, died September 16, 2002, after a yearlong struggle with cancer. Reiter, known throughout the world as "Ray," made foundational contributions to artificial intelligence, knowledge representation and databases, and theorem proving. Reiter, known throughout the world as "Ray," made foundational contributions to artificial intelligence, knowledge representation and databases, and theorem proving. Ray was born in Toronto, Canada, in 1939 to immigrant parents who came from Poland. He received a B.S. in mathematics from the University of Toronto in 1961 and an M.S. degree in mathematics in 1963 from the University of Toronto.

JOHN GASCHNIC, used to come into my office quite regularly for conversations that helped calibrate my mental compass. John had definite opinions about many subjects-about how N research ought, to be pursued, about high standards of achievement, about the need for first-rate equipment, about important research topics, about personnel mattersin short, about all the sorts of things that concern me. John was persuasive and not easily deflected. Well, actually, he couldn't be deflected at all! Usually, I agreed with John and welcomed the added strength that he gave me. Sometimes, though, I might try to explain that something he was advocating wasn't "realistic."