Building a state of the art AI to play Magic: The Gathering
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Building a state of the art AI to play Magic: The Gathering, Melvin Zhang compares different algorithms and iterates on implementations. Initial experiments show Monte Carlo tree search (MCTS) outperforms minimax when given more thinking time. Later work focuses on developing an honest, non-cheating MCTS for Magarena that does not require all cores for parallelization. Open problems remain in improving play when losing and reducing unfun games.
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In the 1930s, Alan Turing developed the concept of a universal Turing machine that could simulate any other Turing machine by reading the description of that machine from its tape. This established the idea that computer programs are just descriptions of logic that can be run on any universal machine. Thus, in developing this concept to formalize the limits of mechanical computation, Turing accidentally invented the fundamental idea of software as a set of instructions that can be run on different hardware platforms.
Solving the TSP for warehouses
The document discusses solving the traveling salesman problem (TSP) for warehouse operations. It mentions that the TSP was first solved manually in 1954 and later solved computationally in 2016 using over 178 CPU years. The document also notes that the TSP is NP-hard, and discusses open problems related to extending the TSP to account for random storage and batching multiple orders, as is relevant for companies like Amazon.
Optimize all the things with MiniZinc
This document contains examples of optimization problems modeled and solved using the MiniZinc constraint modeling language. It includes word problems involving finding the ages of people given relationships between their ages, optimizing inventory quantities to minimize cost while meeting a total budget, and a puzzle involving representing a year using letters from a word. MiniZinc is presented as a solver-independent language for modeling constraint satisfaction and optimization problems.
AMKSS Career Conference 2018: Software Engineering
Software can reach lots of people, constantly evolve through short feedback loops, and is made through an iterative process. Becoming a software engineer involves starting to practice programming, learning computer science, and contributing to open source projects. Questions can be directed to the provided contact.
Beating us at our own Games
This document discusses progress in artificial intelligence and challenges for AI, using games as a benchmark for AI abilities. It outlines how AI systems have achieved optimal or expert level play at games like chess through algorithms like minimax search and Monte Carlo tree search. Go is discussed as a particularly challenging game that was conquered by Deepmind's AlphaGo system using a combination of tree search and deep neural networks. Other games like Magic: The Gathering and Hearthstone are mentioned as presenting additional challenges for AI due to incorporating hidden information.
Getting started with open source game playing AIs
This document provides an introduction to open source game AIs, including Stockfish and Pachi for chess, and descriptions of common AI techniques like minimax search, Monte Carlo tree search, and sampling hidden information. It discusses optimal play, game trees, heuristic scores, cutoff thresholds, and the importance of testing AI changes. Examples are given for chess, Go, and Magic: The Gathering.
Programs that Play better than Us
1) Programs like Deep Blue and Stockfish can now defeat the best human chess players by using algorithms like minimax search and alpha-beta pruning to evaluate game trees up to dozens of ply deep.
2) Go programs initially struggled compared to chess programs due to the enormous game tree of Go. But programs like Crazy Stone and more recently AlphaGo used Monte Carlo tree search combined with deep neural networks to achieve superhuman performance at Go.
3) While algorithms like minimax and Monte Carlo tree search have achieved superhuman performance at perfect-information games like chess and Go, developing strong algorithms for imperfect-information games like poker remains an open research problem. Determinization is one approach but has limitations.
Building a Turing Machine emulator to explore Turing's great ideas
This document discusses Alan Turing's ideas about computation and Turing machines. It proposes building a Turing machine emulator using Lego to explore Turing's ideas. Turing machines provide a precise definition of computation, and Turing showed that a universal Turing machine could compute any computable sequence, meaning a single machine could emulate all others. Turing also introduced the idea of programs as data that could be used as input for other programs.
Functional programming from first principles
This document is a presentation on functional programming from first principles. It introduces lambda calculus notation for expressions and values, lists the presenter's contact information and references, and covers topics like Church's thesis, laziness vs eagerness, typing, and further readings on the subject. It concludes by providing information on a Meetup group for Clojure.
Binary Lambda Calculus and Combinatory Logic
The document discusses binary lambda calculus and combinatory logic. It provides an overview of related topics like algorithmic information theory, the Mandelbrot set, lambda calculus, combinatory logic, and their histories developed by thinkers like Alonzo Church, Haskell Curry, and John Tromp. It also provides further reading resources for exploring these topics in more depth.
AMKSS Career Conference 2015: Programming
The document provides information about an upcoming career conference in 2015 and lists several prerequisites and resources for attendees. It recommends leveraging open communication platforms and mentions examples like Slack replacing email in some workplaces. It also honors Grace Hopper as a pioneering computer scientist and lists some coding projects for learning including porting Android to new devices and developing a multiplayer gaming arena. In summary, the career conference will focus on software trends, recommend staying up to date on new technologies, and provide opportunities for learning and networking.
Efficient Selectivity and Backup Operators in Monte-Carlo Tree Search
This document discusses efficient selection and backup operators in Monte-Carlo Tree Search. It describes the selection principle where moves that look best are searched deeper, while bad moves are searched less. It also explains that Crazy Stone selects each move probabilistically based on the move's statistics. Finally, it mentions that Crazy Stone uses a "Mix" backup operator that takes a linear combination of the mean, max, and robust max values of child nodes to update internal nodes after simulation.
Quest for the optimal algorithm
The document discusses Melvin Zhang's research project on finding gene teams. It provides an overview of conserved gene clusters and gene teams. Melvin Zhang's project involves developing an algorithm called FindTeams that can compute the gene teams of two permutations in O(n lg^2 n) time. Over the years, Melvin Zhang and other researchers worked to improve the algorithm's time complexity for this problem and related problems such as constructing gene team trees.
Becoming a better problem solver: a CS perspective
This document discusses strategies and tactics for becoming a better problem solver from a computer science perspective. It begins by defining problem solving and outlining common steps. It then presents strategies for getting started, such as getting hands-on, restating the problem, and using wishful thinking to simplify issues. Tactics for making progress are also outlined, including considering extremes, exploiting symmetries, and trading space for time. Examples are provided to illustrate the approaches. The overall message is that varying attempts, recording progress, and finding easier related problems to solve can help overcome challenges.
Ortholog assignment
The document discusses computational methods for predicting orthologs, or genes in different species that evolved from a common ancestral gene. It describes three main approaches: graph-based, tree-based, and rearrangement-based. The most popular method is bidirectional best hit (BBH), which identifies orthologs as the best reciprocal matches between species. The document also proposes a new method called BBH-LS that combines BBH with a local synteny score to improve ortholog prediction accuracy. It presents results testing various methods on human-mouse and human-rat datasets that suggest BBH-LS performs better than existing alternatives.
Building pipelines with Make
This document discusses using Makefiles to build pipelines for bioinformatics projects. It describes how Makefiles provide a declarative way to specify dependencies between targets and commands to build them. This allows Make to efficiently rebuild only what is necessary after changes. The document explains basic Makefile structure and features like automatic variables, pattern rules, and using descriptive filenames to generalize rules for different projects. Using Makefiles avoids re-running all steps manually and helps manage complex bioinformatics workflows.
Opportunities in STEM
This document discusses opportunities in STEM fields such as science, technology, engineering and mathematics. It outlines various disciplines within these broad fields including physics, chemistry, biology, mathematics, engineering subjects like mechanical, chemical and various types of biomedical engineering. It also notes that biomedical companies invested over $800 million in Singapore in 2009 and that Singapore is a top 10 petrochemical hub in the world.
More from Melvin Zhang (17)
AMKSS Career Conference 2018: Software Engineering
AMKSS Career Conference 2018: Software Engineering
Building a Turing Machine emulator to explore Turing's great ideas
Building a Turing Machine emulator to explore Turing's great ideas
Efficient Selectivity and Backup Operators in Monte-Carlo Tree Search
Efficient Selectivity and Backup Operators in Monte-Carlo Tree Search
Becoming a better problem solver: a CS perspective
Becoming a better problem solver: a CS perspective
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Building a state of the art AI to play Magic: The Gathering
- 1. Building a state of the art AI to play Melvin Zhang
- 2. http://meowthegathering.tumblr.com/post/96620097852/mirri-debuted-in-exodus-but-still-looks-like-a
- 11. 1. Solve a simpler problem
- 14. 2. Choose the right algorithm
- 22. Comparison of cheating AIs At 1s thinking time: Minimax MCTS 1 0.84
- 23. Comparison of cheating AIs At 1s thinking time: Minimax MCTS 1 0.84 At 4s thinking time: Minimax MCTS 1 1.53
- 24. Date: Sun, 4 Mar 2012 21:04:39 +0800 From: Melvin Zhang To: P. I. Cowling Subject: Applying MCTS to make all AI decisions ... Our own internal comparison with the original minimax AI confirms that MCTS has better performance than minimax. I hope that our preliminary findings and implementation would be helpful to you and your team in further research in this area. Regards, Melvin
- 25. 3. Iterate, iterate, iterate
- 26. Monte-Carlo Tree Search without cheating Issue 92: Reported by eolebigot, Nov 12, 2011 Would it be possible for Magarena to have a *non-cheating* Monte-Carlo Tree Search? I tend to avoid the cheating AI, because I prefer fair games, so an MCTS that does not cheat would be great! https://code.google.com/p/magarena/issues/detail?id=92
- 27. Date: Thu, 30 Aug 2012 12:33:13 +0100 From: P. I. Cowling To: Melvin Zhang Subject: Re: Applying MCTS to make all AI decisions ... We remain interested in MCTS for Magic:The Gathering... see our new paper at http://scim.brad.ac.uk/staff/pdf/picowlin/CowlingWardPowley2012.pdf Cheers, Peter
- 30. Monte-Carlo AI not fully parallel Issue 344: Reported by eolebigot, Apr 28, 2013 For the cheating Monte-Carlo AI, 2 cores are used (instead of 4 thanks to hyperthreading). While this may be normal, this is quite unusual. For the non-cheating Monte-Carlo AI, only 1 core is used. https://code.google.com/p/magarena/issues/detail?id=344
- 32. Comparison of honest AIs At 1s thinking time: Minimax MCTS 1 0.88 (0.84)
- 33. Comparison of honest AIs At 1s thinking time: Minimax MCTS 1 0.88 (0.84) At 4s thinking time: Minimax MCTS 1 1.71 (1.53)
- 34. Open problems
- 35. Open problems MCTS makes bad plays when it is losing.
- 36. Open problems MCTS makes bad plays when it is losing. Reduce occurrence of games which are not fun.
- 37. www.firemind.ch
- 38. www.untap.in