5000 Episodes And No Commercials

Groundbreaking! Does for TV shows what Leonard Maltin’s guides do for movies! Forget movies! Sales of TV DVDs are outpacing all other categories, according to Video Store magazine. The Simpsons, 24, Lost, Desperate Housewives, Alias, even old chestnuts like Columbo and Home Improvement are blowing out of the stores as fans and collectors rush to buy their favorite shows, compact and complete. How do buyers know which shows are the best, which season contains that favorite moment, which episode features that guest star? They don’t—not without their trusty copy of 5,000 Episodes No Commercials which gives full information on every sitcom and drama released on DVD, whether in season-by-season sets, individual episodes, best-of compilations, specials, or made-for-TV movies. Almost 500 pages of listings include year of original airing, information on audio and video quality, extras, Easter eggs, and more. Every couch potato is sure to heave up off the sofa just long enough to buy 5,000 Episodes No Commercials!

This bibliographic guide covers the “Buffyverse”—the fictional worlds of the acclaimed television series Buffy the Vampire Slayer (1997–2003) and its spinoff Angel (1999–2004), as well as the original Buffy feature film of 1992. It is the largest and most inclusive work of its kind. The author organizes and describes both the original texts of the Buffyverse (episodes, DVDs, novels, comic books, games, and more) and the secondary materials created about the shows, including books, essays, articles, documentaries, dissertations, fan production and websites. This vast and diverse collection of information about these two seminal shows and their feature-film forebear provides an accessible, authoritative and comprehensive survey of the subject.

Presents a guide to the names and specialities of American and Canadian publishers, editors, and literary agents, including information on the acquisition process and on choosing literary agents.

Reinforcement learning has developed as a successful learning approach for domains that are not fully understood and that are too complex to be described in closed form. However, reinforcement learning does not scale well to large and continuous problems. Furthermore, acquired knowledge specific to the learned task, and transfer of knowledge to new tasks is crucial. In this book the author investigates whether deficiencies of reinforcement learning can be overcome by suitable abstraction methods. He discusses various forms of spatial abstraction, in particular qualitative abstraction, a form of representing knowledge that has been thoroughly investigated and successfully applied in spatial cognition research. With his approach, he exploits spatial structures and structural similarity to support the learning process by abstracting from less important features and stressing the essential ones. The author demonstrates his learning approach and the transferability of knowledge by having his system learn in a virtual robot simulation system and consequently transfer the acquired knowledge to a physical robot. The approach is influenced by findings from cognitive science. The book is suitable for researchers working in artificial intelligence, in particular knowledge representation, learning, spatial cognition, and robotics.