A computer program that uses AI planning techniques is now the world champion computer program in the game of Contract Bridge. As reported in The New York Times and The Washington Post, this program--a new version of Great Game Products' The classical approach used in AI programs for games of strategy is to do a game tree search using the well-known minimax formula (eq. 1) The minimax computation is basically a bruteforce search: If implemented as formulated here, it would examine every node in the game tree. In practical implementations of minimax game tree searching, a number of techniques are used to improve the efficiency of this computation: putting a bound on the depth of the search, using alpha-beta pruning, doing transposition-table lookup, and so on. However, even with enhancements such as these, minimax computations often involve examining huge numbers of nodes in the game tree. Because a Bridge hand is typically played in just a few minutes, there is not enough time for a game tree search to search enough of this tree to make good decisions.
Already home to some of the world's most innovative artificial intelligence companies, the UK has a rich ecosystem of investors, employers, developers and clients. These data sets can then be processed to create algorithms to drive machine learning – enabling computers to interpret data, predict outcomes and deliver solutions autonomously. Businesses must gather the right data, format it and then interpret it intelligently before creating algorithms to drive service or product lines and deliver solutions. This shouldn't be a hindrance, however, as it's the utilisation of smart algorithms and big data that's key to advancing machine learning.
Not too long ago, robots were considered a possible but surreal feature of a distant future. But take stock for a moment and it's clear that artificial intelligence and machine learning has already pervaded our lives. From high-frequency trading in financial markets to customised playlists on Spotify, machines are able to receive, process and act upon data intelligently. Machine learning and artificial intelligence are at the forefront of drastic change, and the world's largest technology companies, from Google and Amazon to Dyson, are focussed on harnessing artificial intelligence to revolutionise business and consumer services. Recognising its sea-change capabilities, the British Government is also committed to advancing artificial intelligence, predicting it could add £654 billion to the UK economy by 2035.
Tom Throop knows a lot about computers and the game of bridge. Back in 1958, while working at a U.S. Navy lab in the District, he programmed a Univac computer to play the game. Later, he designed bridge software for Radio Shack, Apple and Commodore computers. Eventually, he founded a company in Bethesda called Great Game Products Inc. that focused on selling his Bridge Baron software. But when Throop, 64, wanted to make the Bridge Baron a better player -- it lacked the ability to develop a strategy at the beginning of a game -- he knew he'd have to get some outside help in the world of artificial intelligence.
Bridge Baron is a computer program that plays bridge. It won the 1997 world championship of computer bridge, the Baron Barclay World Bridge Computer Challenge, as reported in The New York Times and The Washington Post. The five-day competition, which was hosted by the American Contract Bridge League in July 1997, included five computer programs, from the US, Japan, and Germany. The Bridge Baron won every head-to-head match that it played against the other programs.
Despite some success of Perfect Information Monte Carlo Sampling (PIMC) in imperfect information games in the past, it has been eclipsed by other approaches in recent years. Standard PIMC has well-known shortcomings in the accuracy of its decisions, but has the advantage of being simple, fast, robust and scalable, making it well-suited for imperfect information games with large state-spaces. We propose Presumed Value PIMC resolving the problem of overestimation of opponent's knowledge of hidden information in future game states. The resulting AI agent was tested against human experts in Schnapsen, a Central European 2-player trick-taking card game, and performs above human expert-level.
As adversarial environments become more complex, it is increasingly crucial for agents to exploit the mistakes of weaker opponents, particularly in the context of winning tournaments and competitions.In this work, we present a simple post processing technique, which wecall Perfect Information Post-Mortem Analysis (PIPMA), that can quickly assess the playing strength of an opponent in certain classes of game environments. We apply this technique to skat, a popular German card game, and show that we can achieve substantial performance gains against not only players weaker than our program, but against stronger players as well. Most importantly, PIPMA can model the opponent after only a handful of games. To our knowledge, this makes our work the first successful example of an opponent modelling technique that can adapt its play to a particular opponent in real time in a complex game setting.
In conjunction with the Association for the Advancement of Artificial Intelligence's Hall of Champions exhibit, the Innovative Applications of Artificial Intelligence held a panel discussion entitled "AI Game-Playing Techniques: Are They Useful for Anything Other Than Games?" This article summarizes the panelists' comments about whether ideas and techniques from AI game playing are useful elsewhere and what kinds of game might be suitable as "challenge problems" for future research.