Windows could not search for new updates

Add me to the list of annoyed users who are receiving Code 80244FFF with no resolution from Microsoft (at least, Microsoft does not seem to have acknowledged the problem).

WindowsUpdate

I’ve tried the following options so far, and am open to other suggestions:

Workaround

Update: After applying the Windows 8.1 Update, I now receive Code 80240442. So all I’ve really done is trade one error code for another.

There does seem to be a workaround for this issue, but it involves temporarily modifying your Internet Options when wanting to run Windows Update. First, find Internet Options, either through the Start Menu or under Control Panel:

Internet Options

Then, go under the Connections tab and click LAN settings. Check “Use a proxy server for your LAN”:

LAN Settings

Click Advanced, and enter the following settings:

Proxy Settings

For HTTP and Secure, use 127.0.0.1 for the “Proxy address to use” and 8888 for the port. Under Exceptions, type <-loopback>.

You may have to reboot your machine. Then, run Windows Update as you normally would.

Frontiers in Education

Frontiers in Education This week I attended the Frontiers in Education conference (October 23-26) in Oklahoma City, Oklahoma, where I presented my work on A Community College Blended Learning Classroom Experience through Artificial Intelligence in Games. This is my first Computer Science Education paper.

The paper reports on the experience of teaching an industry-validated course on Artificial Intelligence in Computer Games within the Simulation and Game Design department at a two-year community college during a 16-week semester. The course format used a blended learning just-in-time teaching approach, which included active learning programming exercises and one-on-one student interactions.

ACT-R Models of the Concentration Game

Concentration Our paper on Speed/Accuracy Tradeoff in ACT-R Models of the Concentration Game Game has appeared in the 2013 International Conference on Cognitive Modeling.

This paper describes the development of subsymbolic ACT-R models for the Concentration game. Performance data is taken from an experiment in which participants played the game under two conditions: minimizing the number of mismatches/turns during a game, and minimizing the time to complete a game. Conflict resolution and parameter tuning are used to implement an accuracy model and a speed model that capture the differences for the two conditions. Visual attention drives exploration of the game board in the models. Modeling results are generally consistent with human performance, though some systematic differences can be seen. Modeling decisions, model limitations, and open issues are discussed.

Press: Teaching a Computer to Play ‘Concentration’ Advances Security, Understanding of the Human Mind