Bridge


Computer Bridge

AI Magazine

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.


Rise of the Robots in Finance Transformation - Eton Bridge Partners

#artificialintelligence

Of course, when we talk about robots in the business process context, we need to forget the old clichés of R2-D2, Wall-E or the Terminator. Or even the real-life robots used in manufacturing.


Are Robots Changing the Face of Finance? I Eton Bridge Partners

#artificialintelligence

MS: Chris, two of the hottest topics in business and finance automation at the moment are'Robots' and'Artificial Intelligence'. They're almost used interchangeably, but I understand they're not the same. Can you explain the difference?


How artificial intelligence is redefining our world - Eton Bridge Partners

#artificialintelligence

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.


How artificial intelligence is redefining our world - Eton Bridge Partners

#artificialintelligence

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.


Program for a Better Bridge Game; A College Partnership Aids Industry Research - The Washington Post

AITopics Original Links

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.


World Champion Bridge Program

AITopics Original Links

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.


An Expert-Level Card Playing Agent Based on a Variant of Perfect Information Monte Carlo Sampling

AAAI Conferences

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.


Contract Bridge Bidding by Learning

AAAI Conferences

Contract bridge is an example of an incomplete information game for which computers typically do not perform better than expert human bridge players. In particular, the typical bidding decisions of human bridge players are difficult to mimic with a computer program, and thus automatic bridge bidding remains to be a challenging research problem. Currently, the possibility of automatic bidding without mimicking human players has not been fully studied. In this work, we take an initiative to study such a possibility for the specific problem of bidding without competition. We propose a novel learning framework to let a computer program learn its own bidding decisions. The framework transforms the bidding problem into a learning problem, and then solves the problem with a carefully designed model that consists of cost-sensitive classifiers and upper-confidence-bound algorithms. We validate the proposed model and find that it performs competitively to the champion computer bridge program that mimics human bidding decisions.


Real-Time Opponent Modelling in Trick-Taking Card Games

AAAI Conferences

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.