Ting-Han Wei

Human vs. Computer Go: Review and Prospect [Discussion Forum]

The Google DeepMind challenge match in March 2016 was a historic achievement for computer Go development. This article discusses the development of computational intelligence (CI) and its relative strength in comparison with human intelligence for the game of Go. We first summarize the milestones achieved for computer Go from 1998 to 2016. Then, the computer Go programs that have participated in previous IEEE CIS competitions as well as methods and techniques used in AlphaGo are briefly introduced. Commentaries from three high-level professional Go players on the five AlphaGo versus Lee Sedol games are also included. We conclude that AlphaGo beating Lee Sedol is a huge achievement in artificial intelligence (AI) based largely on CI methods. In the future, powerful computer Go programs such as AlphaGo are expected to be instrumental in promoting Go education and AI real-world applications.

Job-level algorithms for Connect6 opening position analysis

This paper investigates job-level (JL) algorithms to analyze opening positions for Connect6. The opening position analysis is intended for opening book construction, which is not covered by this paper. In the past, JL proof-number search (JL-PNS) was successfully used to solve Connect6 positions. Using JL-PNS, many opening plays that lead to losses can be eliminated from consideration during the opening game. However, it is unclear how the information of unsolved positions can be exploited for opening book construction. For this issue, this paper first proposes four heuristic metrics when using JL-PNS to estimate move quality. This paper then proposes a JL upper confidence tree (JL-UCT) algorithm and some heuristic metrics, one of which is the number of nodes in each candidate moves subtree. In order to compare these metrics objectively, we proposed two kinds of measurement methods to analyze the suitability of these metrics when choosing best moves for a set of benchmark positions. The results show that for both metrics this node count heuristic metric for JL-UCT outperforms all the others, including the four for JL-PNS.

Job-level computing with BOINC support

Recently, Wu et al. introduced a general approach based on distributed computing named Job-Level (JL) Computing. JL Computing has been successfully used to construct the opening books of game-playing programs.? In order to support large-scale computing problems such as solving 7×7 killall-Go, or building opening books for 9×9 Go or Connect6, record databases are used to store JL computing results. In this paper, we further design a mechanism that combines the JL computing system with BOINC (Berkeley Open Infrastructure for Network Computing), so that we can leverage more computing power from volunteers to solve even larger problems. A preliminary experiment has been done to demonstrate the feasibility of the design.

Dependency-Based Search for Connect6

Allis proposed dependency-based search (DBS) to solve Go-Moku, a kind of five-in-a-row game. DBS is critical for threat space search (TSS) when there are many independent or nearly independent TSS areas. Similarly, DBS is also important for the game Connect6, a kind of six-in-a-row game with two pieces per move. Unfortunately, the rule that two pieces are played per move in Connect6 makes DBS extremely difficult to apply to Connect6 programs. This paper is the first attempt to apply DBS to Connect6 programs. The targeted program is NCTU6, which won Connect6 tournaments in the Computer Olympiad twice and defeated many professional players in Man-Machine Connect6 championships. The experimental results show that DBS yields a speedup factor of 4.12 on average, and up to 50 for some hard positions.