All posts by Tim himself

As If On Cue…

Merchant of VenusThe same day I was posting about the planned reprints of Merchant of Venus, friends of mine were clearing some games out of their shelves … including a copy of Merchant of Venus. One day earlier, and I would have had to turn it down and tell them how much those things were worth (in fact, I passed up a copy of Hamblen’s Magic Realm not so long ago for that reason). As it is, well, it’s a vintage game and much appreciated, but no longer one I’d have to feel guilty about accepting.

Of course, this does no good for the list of board games that I own but have not played, especially with Flash Point and the special edition of Glory to Rome already ordered, not to mention Risk: Legacy on the horizon. Keeping that under control is a project that’s been making negative progress.

Merchant of Venus Reprint Announced … One Time Too Many

Merchant of VenusWell, this is awkward.

Richard Hamblen’s Merchant of Venus is one of the great out-of-print holy grails of the hobby boardgame world. Originally published by Avalon Hill in 1988, copies these days will run you in the $200 range. So, it was good news when Stronghold Games announced that they had forged an agreement with Hamblen for a reprint of the game. Unless, that is, you were Fantasy Flight Games, for whom it was terrible news, since it turns out they had licensed the same rights from Hasbro (now-parent company of the defunct Avalon Hill). Continue reading Merchant of Venus Reprint Announced … One Time Too Many

Satisfaction Part 2

As I mentioned yesterday, one of the weaknesses of a work-to-schedule AI approach, with an exponentially growing search space, is that you can easily spend most of your time on an analysis that never ends up getting used. After all, if at each search depth you take more time than all of the previous depths put together, there’s no point starting a search in the whole second half of your allotted time. Continue reading Satisfaction Part 2

You Get What You Need

stopwatchIf I recently taught my AI to despair, then currently I’m teaching it satisfaction.

See, a planning AI is often about look-ahead. The more turns in the future it can consider, the smarter its answer is apt to be. But, of course, that takes time – exponentially increasing time, in fact. So, how far ahead can you afford to look, and still finish on a tolerable schedule? Continue reading You Get What You Need