Author: Michael Yee
Date: 11:50:41 05/12/05
Go up one level in this thread
On May 12, 2005 at 12:58:00, Matthew Hull wrote: >On May 12, 2005 at 12:31:05, Steven Edwards wrote: >>On May 12, 2005 at 12:18:10, Matthew Hull wrote: >>>On May 12, 2005 at 12:09:11, Steven Edwards wrote: >>>>On May 12, 2005 at 11:54:16, Matthew Hull wrote: >> >>>So, the initial test shows some success. You also indicate in another post that >>>a more comprehensive problem set will be the next step, at which point you will >>>have a mate-attack organism thingy with which to work. How many other organisms >>>do you estimate will be needed to get a basic cognitive process to start taking >>>over from the toolkit? Do you have a preliminary list of these in mind? >> >>I have it working on the 1,001 position suite BWTC. Later, I'll try automated >>construction of a multithousand position suite from appropriate PGN data. >> >>Symbolic has a knowledge sequencer named KsSurveyor whose job is to identify >>strategic themes in a position. Each recognized theme is posted as a Theme >>instance in the Instance Database in the position search tree node for that >>position which is later used by the rest of the planning process. My idea at >>the moment is to have (at least) one species/organism for each theme. This >>organism will be used to determine theme matching, any moves that will promote >>the theme (along with ranking), and (via a one ply search) help determine >>countermoves that work against the theme. >> >>The list of themes will be stolen from various chess books I have. Some themes >>like MateAttack are simple in that they aren't parameterized. Other themes will >>have target, region, and sequencing parameters. > > >I see this process as similar to NN technology in terms of the utilization of >processing resources. The bulk of the compute resource are consumed ahead of >time, and the generalized "understanding" stored for later reference. The >application of these learned things will require further effort in a game, but >most of the effort will have already been computed and stored in generalized >form. A traditional AB searcher remembers almost nothing and must re-compute >it's "knowledge" frequently and afterward remembers nothing, except what may be >in the transient hash table. The exception is the EGTB, whose compute resources >were already consumed and don't need recalculation. The EGTB is the only thing >"remembered" between games, except some basic position/book learning. But even >the hash table and EGTB are not really "understanding", but just rote memeory. > >The conservation of computing effort is what I like most about your approach, >and other approaches like NN. The observation about storing knowledge/generalization ability is nice. But I think there are some key differences between neural networks and genetic algorithms. NNs are merely a class of functions/models with parameters. (Well, their structure happens to be able to construct new features from more basic ones, which is neat.) The parameters can be fit/learned using backpropagation or any other optimization technique. A GA is merely a pretty robust optimization technique that can be used to fit the parameters of a model (e.g., a NN), or, in general, optimize any objective function. In Steven's case (I think), he chose subsets of microfeatures (templates) as his family of models and used a GA to optimize an objective function (test suite performance) over possible models. A key difference between his family of models and NNs is that NNs are hierarchical. Michael
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