Archive for the ‘Knowledge Retention’ Category

DeepView: Computational Tools for Chess Spectatorship [Knowledge Retention?]

Sunday, October 19th, 2014

DeepView: Computational Tools for Chess Spectatorship by Greg Borenstein, Prof. Kevin Slavin, Grandmaster Maurice Ashley.

From the post:

DeepView is a suite of computational and statistical tools meant to help novice viewers understand the drama of a high-level chess match through storytelling. Good drama includes characters and situations. We worked with GM Ashley to indentify the elements of individual player’s styles and the components of an ongoing match that computation could analyze to help bring chess to life. We gathered an archive of more than 750,000 games from chessgames.com including extensive collections of games played by each of the grandmasters in the tournament. We then used the Stockfish open source chess engine to analyze the details of each move within these games. We combined these results into a comprehensive statistical analysis that provided us with meaningful and compelling information to pass on to viewers and to provide to chess commentators to aid in their work.

The questions we answered include:

In addition to making chess more accessible to novice viewers, we believe that providing access to these kinds of statistics will change how expert players play chess, allowing them to prepare differently for specific opponents and to detect limitations or quirks in their own play.

Further, we believe that the techniques used here could be applied to other sports and games as well. Specifically we wonder why traditional sports broadcasting doesn’t use measures of significance to filter or interpret the statistics they show to their viewers. For example, is a batter’s RBI count actually informative without knowing whether it is typical or extraordinary compared to other players? And when it comes to eSports with their exploding viewer population, this approach points to rich possibilities improving the spectator experience and translating complex gameplay so it is more legible for novice fans.

A deeply intriguing notion of mining data to extract patterns that are fashioned into a narrative by an expert.

Participants in the games were not called upon to make explicit the tacit knowledge they unconsciously rely upon to make decisions. Instead, decisions (moves) were collated into patterns and an expert recognized those patterns to make the tacit knowledge explicit.

Outside of games would this be a viable tactic for knowledge retention? Not asking employees/experts but recording their decisions and mining those for later annotation?

ROCK, RACK And Knowledge Retention

Friday, April 18th, 2014

Roundtable on Knowledge Retention Techniques held on 21 May 2013.

From the post:

Back in May 2013, we held a Roundtable on Knowledge Retention Techniques. Carla Newman, Shaharudin Mohd Ishak and Ashad Ahmed very graciously shared with us their journey and experiences in Knowledge Retention.

Three videos, Carla Newman on ROCK (Retention of Critical Knowledge), Shaharudin Mohd Ishak on IE SingaporeÔÇÖs RACK (Retention of All Critical Knowledge), and Ashad Ahmend on Knowledge Retention.

Any knowledge problem of interest to Shell Oil Company is of interest to me! ­čśë

At what junctures in a knowledge retention process would topic maps have the greatest impact?

Not really interested in disrupting current approaches or processes but in discovering where topic maps could be a value add to existing systems.