PC AI sucks at Civilization, reads manual, starts kicking ass The Massachusetts institute of technology have been experimenting with their computers' AI. Specifically the way they deal with the meaning of words. You might think that the best way to analyse this kind of thing would be with a human to PC conversation, like in Short Circuit. That's not the case. Instead, the boffins handed over PC classic, Civilization, and let the AI get on with it. Then the researchers handed over the instructions and taught the PCs a "machine-learning system so it could use a player's manual to guide the development of a game-playing strategy." Associate professor of computer science and electrical engineering, Regina Barzilay, offered insight into why they used a game manual to prove their point. Civ was picked because it's a really fun game, and they didn't want the computers to get bored during the testing. Not really. These kind of systems could make developer's jobs a lot easier. What's the best AI you've ever played against? (via Reddit)
Programming Poker AI This article was originally published in the “Inner Product” column in Game Developer Magazine, November 2005 I recently programmed the AI for the World Series of Poker, developed by Left Field Productions and published by Activision. I started out thinking it would be an easy task. But it proved a lot more complex than I initially thought. This article for the budding poker AI programmer provides a foundation for a simple implementation of No-Limit Texas Holdem Poker AI, covering the basics of hand strength evaluation and betting. The goal of any game playing AI is twofold. You will need an implementation of the following data types. A "suit" is an integer in the range 0..3, where 0=Clubs, 1=Diamonds, 2=Hearts, 3=Spades A "rank" is an integer in the range 0..12, where 0 = 2 (deuce), 1 = 3, 11 = King, 12 = Ace. A "card" is an integer in the range 0..51, hence card = suit*13 + rank. A "Hand" is a 52 bit data type, where each bit represents a single card. The solution is automated testing.
The Coming Technological Singularity ==================================================================== The Coming Technological Singularity: How to Survive in the Post-Human Era Vernor Vinge Department of Mathematical Sciences San Diego State University (c) 1993 by Vernor Vinge (Verbatim copying/translation and distribution of this entire article is permitted in any medium, provided this notice is preserved.) This article was for the VISION-21 Symposium sponsored by NASA Lewis Research Center and the Ohio Aerospace Institute, March 30-31, 1993. It is also retrievable from the NASA technical reports server as part of NASA CP-10129. A slightly changed version appeared in the Winter 1993 issue of _Whole Earth Review_.
Face Detection Homepage: Face finding and recognition Turing Test [<< | Prev | Index | Next | >>] Turing Test Human: Hello Computer: Hi. Human: What's your name? Computer: Can we cut the small talk and get to something interesting? Human: Ur, ok... [<< | Prev | Index | Next | >>] Four Years Later By Mike Deering The date is four years from now, and the world is on the verge of something wonderful. The big news of the last twelve months is the phenomenal success of Ben Goertzel's Novamente program. It has become a super tool for solving complex problems. Nevertheless, the success of the Novamente system has made Ben Goertzel rich and famous, making frequent appearances on the talk show circuit, as well as, visits to the White House. The Novamente phenomena has triggered an explosion of public interest and research in AI. In their posh new building in Atlanta, we find Eliezer working with the seedai system of his own design. "I am going to read you a short story." he says. "Okay." replies the computer. "Mary had a little lamb, whose fleece was white as snow." "The story was about a girl, and a sheep, and a school, and a ..." Interrupting the computer, "What was the girl's name?" "Mary." responds the computer. "What is your name?" Sabine comes through the door, "How's it going?" "Great!
AI Cracks Mystery of 4-Millennia-Old Code (Partially)AI Cracks Mystery of 4-Millennia-Old Code (Partially) Those of you who watched Terminator as a warning of the future may want to pack your bags: an artificial intelligence has just beaten the best human experts at a problem, one that some of them said was utterly impossible. We recommend bringing some EMP weapons, and we hear that there's a nice spot for a city near the Earth's core. The problem was that of the Indus script, writings left behind by the people of the Indus Valley four millennia ago - people thoughtless enough not to drop a dictionary into their ruins. "They've tried to connect it to everything from Egyptian to Easter Island text, so they don't know what the hell it is"-kind-problems, with no success, and five years ago linguists started to claim it wasn't a language at all but a sequence of pretty pictures. Among the languages linked to the mysterious script are Chinese Lolo, Sumerian, Egyptian, Dravidian, Indo-Aryan, Old Slavic, even Easter Island — and, ultimately, no language at all.
Agents An agent is an animate entity that is capable of doing something on purpose. That definition is broad enough to include humans and other animals, the subjects of verbs that express actions, and the computerized robots and softbots. But it depends on other words whose meanings are just as problematical: animate, capable, doing, and purpose. The task of defining those words raises questions that involve almost every other aspect of ontology. Animate. Psychology of Agents Linguistically, an agent is an animate being that can perform some action, and an action is an event that is initiated or carried out by some animate being. The word animate comes from the Latin anima, which means breath or soul. We must inquire for each kind of living thing, what is its psyche; what is that of a plant, and what is that of a human or a beast. Aristotle's hierarchy of functions was based on his extensive study of the plants and animals known in his day. Competence Levels Avoiding. Artificial Psyches
Neuro Evolving Robotic Operatives Neuro-Evolving Robotic Operatives, or NERO for short, is a unique computer game that lets you play with adapting intelligent agents hands-on. Evolve your own robot army by tuning their artificial brains for challenging tasks, then pit them against your friends' teams in online competitions! New features in NERO 2.0 include an interactive game mode called territory capture, as well as a new user interface and more extensive training tools. NERO is a result of an academic research project in artificial intelligence, based on the rtNEAT algorithm. It is also a platform for future research on intelligent agent technology. Currently, we are developing an open source successor to NERO , OpenNERO , a game platform for AI research and education.
Thinking Machine 4 Thinking Machine 4 explores the invisible, elusive nature of thought. Play chess against a transparent intelligence, its evolving thought process visible on the board before you. The artwork is an artificial intelligence program, ready to play chess with the viewer. If the viewer confronts the program, the computer's thought process is sketched on screen as it plays. A map is created from the traces of literally thousands of possible futures as the program tries to decide its best move. Those traces become a key to the invisible lines of force in the game as well as a window into the spirit of a thinking machine. Play the game. Image Gallery View a range of still images taken from Thinking Machine 4. About the work More information about the project and answers to common questions. Credits Created by Martin Wattenberg, with Marek Walczak. About the artists Martin Wattenberg's work centers on the theme of making the invisible visible.