Artificial Intelligence: Foundations of Computational Agents [Poole, David L., Mackworth, Alan K.] on Amazon.com. *FREE* shipping on qualifying offers. Artificial Intelligence: Foundations of Computational Agents

TCSPs (Temporal Constraint Satisfaction Problems), as defined in [Dechter et al., 1991], get rid of unary constraints by binarising them after having added an "origin of the world" variable. The constraints are therefore exclusively binary; additionally, a TCSP verifies the property that it is node-consistent and arc-consistent. Path-consistency, the next higher local consistency, solves the consistency problem of a convex TCSP, referred to in [Dechter et al., 1991] as an STP (Simple Temporal Problem); more than that, the output of path-consistency applied to an n+1-variable STP is a minimal and strongly n+1-consistent STP. Weaker versions of path-consistency, aimed at avoiding what is referred to in [Schwalb and Dechter, 1997] as the "fragmentation problem", are used as filtering procedures in recursive backtracking algorithms for the consistency problem of a general TCSP. In this work, we look at the constraints between the "origin of the world" variable and the other variables, as the (binarised) domains of these other variables. With this in mind, we define a notion of arc-consistency for TCSPs, which we will refer to as binarised-domains Arc-Consistency, or bdArc-Consistency for short. We provide an algorithm achieving bdArc-Consistency for a TCSP, which we will refer to as bdAC3, for it is an adaptation of Mackworth's [1977] well-known arc-consistency algorithm AC3. We show that bdArc-Consistency computes the minimal (binarised) domains of an STP. We then show how to use the result in a general TCSP solver, in a TCSP-based job shop scheduler, and in generalising the well-known Dijkstra's one-to-all shortest paths algorithm.

About the book: A widely used text on reinforcement learning, which is one of the most active research areas in artificial intelligence, this book provides a clear and simple account of the field's key ideas and algorithms. With a focus on core online learning algorithms, including UCB, Expected Sarsa, and Double Learning, it then extends these ideas to function approximation covering topics on artificial neural networks and the Fourier basis. This second edition includes new chapters on reinforcement learning's relationships to psychology and neuroscience as well as updated case-studies on AlphaGo and AlphaGo Zero, Atari game playing, and IBM Watson's wagering strategy. About the authors: Richard S. Sutton is a distinguished research scientist at DeepMind in Edmonton and a professor in the Department of Computing Science at the University of Alberta. He previously worked in industry at AT&T and GTE Labs, and in academia at the University of Massachusetts.

The British Royal Air Force had a problem. It was 1943, and the Brits were using radar equipment to spot German submarines sneaking around off the western coast of France. The young men sitting in planes circling over the Bay of Biscay had more than enough motivation to keep a watchful eye for the telltale blips on the screens in front of them. Yet they had a worrying tendency to miss the signals they'd been trained to spot. The longer they spent looking at the screen, the less reliable they became.

AI and AAAI are coevolving. As AI matures, its focus is shifting from inward-looking to outwardlooking. Some of the new concerns of the field are social awareness, networking, cross-disciplinarity, globalization, and open access. AAAI must reflect and support those concerns. AI is now a mature discipline.

Of special note was Deep Blue, the IBM computer system that beat then-reigning world chess champion Garry Kasparov. Then there was the boom and bust of expert systems companies, the times when AI was the most popular computer science major, and the 1985 AAAI conference that had over 5,500 attendees. This was followed by what is commonly referred to as the "AI Winter." "This was not a prolonged phenomenon," observed Brachman. Indeed, in the current Presidential budget for fiscal year 2006, the Cognitive Computing Systems line item is $200 million, and the proposed 2007 budget is $240 million.

John Mc-Carthy, then an assistant mathematics professor at Dartmouth, organized the conference and coined the name "artificial intelligence" in his conference proposal. This summer AAAI celebrates the first 50 years of AI; and continues to foster the fertile fields of AI at the National AI conference (AAAI-06) and Innovative Applications of AI conference (IAAI-06) in Boston. The computer age was just dawning in 1956. MIT researchers that year built the TX-0, the first general-purpose, programmable computer with transistors. IBM shipped the first magnetic disk storage, the 305 RAMAC, composed of 50 magnetically coated metal platters with 5 million bytes of data.

The theme of this article is the dynamics of evolution of agents. That theme is applied to the evolution of constraint satisfaction, of agents themselves, of our models of agents, of artificial intelligence, and, finally, of the Association for the Advancement of Artificial Intelligence (AAAI). The overall thesis is that constraint satisfaction is central to proactive and responsive intelligent behavior. I think that everyone who serves begins with great trepidation but leaves with a sense of satisfaction. One of the sources of satisfaction was the opportunity to give the presidential address at AAAI-07 in Vancouver, my hometown.

Machines turning on their creators has been a popular theme in books and movies for decades, but very serious people are starting to take the subject very seriously. Physicist Stephen Hawking says, "the development of full artificial intelligence could spell the end of the human race." Tesla Motors and SpaceX founder Elon Musk suggests that AI is probably "our biggest existential threat." Artificial intelligence experts say there are good reasons to pay attention to the fears expressed by big minds like Hawking and Musk -- and to do something about it while there is still time. Hawking made his most recent comments at the beginning of December, in response to a question about an upgrade to the technology he uses to communicate, He relies on the device because he has amyotrophic lateral sclerosis, a degenerative disease that affects his ability to move and speak.

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