Incorporating boolean data into analysis I have a data set of about 3,000 field observations. 
The data collected is divided into 20 variables (real numbers), 30 boolean variables, and 10 or so look up variables and one "answer" variable
We have about 20,000 objects in the field, and i'm trying to produce an "answer" for the 20,000 objects based on the 3,000 observations.
What are some of the available methods that incorporate booleans and look up tables?
any suggestions on how i should proceed?
EDIT
the answer variable is a boolean as well
EDIT 2
a sample of the variable data:


*

*Age of specimen

*length, area, volume

*time since last inspection

*height

*design life


Lookup table


*

*material type 

*coating type

*design standard

*design effectiveness


a sample of the boolean


*

*is it inspected?

*is it in bad shape

*does it need repairs soon


the answer variable which is my f(x) is:


*

*is it useable

 A: You are decribing "categorical variables" (represented in R a factors). These can be incorporated into almost any statistical model by being assigned levels.  You would need to give more detail about your particular problem in order to be advised on a particular method.  
Edit
If the response variable has two possible outcomes, you might consider binomial or logistic regression. 
Note: If you're not familiar with the different kinds of variables in statistics, I suggest reading the first few chapters of Andrew Gelman's "Data Analysis Using Regression and Multilevel/Hierarchical Models" which covers this in a very understandable manner.
A: It sounds like you are trying to predict your boolean response, yes?
This is called classification.
Logistic Regression is the obvious choice here, but there are other methods too. You can't do traditional regression, because the response is not a real number.
The lookup variables are called nominals, and can be dealt with in regression by using "dummy" variables.
For example, if your lookup variable is type=[steel, aluminum, plastic] (N=3), then your dummy variables would look like this:
IsSteel = [1,0]
IsAlum = [1,0]
There would only be two (N-1) dummy variables, as IsSteel=0 AND IsAlum=0 represents "IsPlastic"=1
But any good stats program should handle this.
If you need a book, I recommend Multivariate Data Analysis by Hair.
A: Ingo Ruczinski has contributed to promote the use of Logic regression for data set consisting of binary variables, with an emphasis on higher-order interaction terms. The main advantage compared to usual or penalized GLMs is that it is more parcimonious in terms of degrees of freedom. The outcome may be continuous or categorical, and continuous covariates can be added to the model (or the outcome can first be residualized on them if these are the binary predictors that are of interest). 
The original paper

Ruczinski I, Kooperberg C, LeBlanc ML
  (2003). Logic Regression.
  Journal of Computational and Graphical Statistics, 12(3), 475-511.

includes several applications in biomedical studies, and a comparison of LR with CART and MARS. Although it has mainly been applied in large-scale genetic studies (e.g. genome-wide association studies), it should work with any binary variables whose combinations of interest can be expressed with a set of logical operators.
The LogicReg R package implements this technique; see also the related packages on CRAN and Bioconductor, esp. LogicForest which shares some ideas with Random Forests.
A: Try Random Forest; from my experience it may perform well on such kind of data, and gives you a some additional interesting information, like variable importance and object similarity measure. 
