Stephen Reed wrote: > Thanks! I will author a paper about early results with Cognitive Cyc when > I have them for submission to this workshop, or to the IJCAI poster > track.
Well, if you go to that conference, maybe we'll meet in person finally ;) > Regarding Cognitive Cyc, I have completed a UML State Machine interpreter > in java that complies with UML v1.4 but stops short of Action Semantics, > rather I use the DynamicJava interpreter to interpret java source > statements when evaluating transition guard conditions, transition > effects, and state entry/exit procedures. Currently I am authoring Cyc > vocabulary to represent the UML objects and relationships, with the goal > of having Cyc construct state machine models in the knowledge base that > can be subsequently interpreted to make Cyc do things. That is very interesting. How do you plan to get there to be a rich collection of connections between Cyc's repository of state machine models, and Cyc's primary database of human common-sense knowledge? I can see how these connections could be built up through experience if Cyc were actually controlling a robotic system, for example. > I have also investigated the JavaBayes Bayesian inference engine and will > connect it to Cyc's Bayesian vocabulary so that Cyc can perform Bayesian > inference. And I have a plan to modestly implement fuzzy inference to > support Cyc's existing fuzzy vocabulary. What kind of approach does JavaBayes use for handling very large Bayesian nets? Gibbs sampling and the other standard MCMC methods seem not to scale very well. The following paper seems to contain a tricky approximation algorithm that works better than the standard Gibbs sampling method, for very large nets... http://citeseer.nj.nec.com/jensen96blocking.html I have not tried this algorithm myself, just read the paper, but the concepts seem solid to me. We don't use Bayes nets for inference in Novamente, we have our own Probabilistic Term Logic probabilistic reasoning system. However, we are exploring the use of Bayes nets inside a substantially modified version of Pelikan and Goldberg's Bayesian Optimization Algorithm http://citeseer.nj.nec.com/pelikan99boa.html which we may use as a component of our parameter optimization and procedure learning components. > And I am beginning to flesh out java classes to implement the NIST/Albus > Reference Architecture which will give Cognitive Cyc its cognitive > behavior -bit by bit. The UML state machine interpreter will be the > the behavioral framework for implementing the Perception, Value > Judgment, and Behavior Generation components of the NIST/Albus Reference > Architecture. I guess that's an OK behavioral framework... the big problem though is how you're going to *learn* perceptual and behavioral schemata ("subprograms") within Cyc. I strongly suspect that Cyc's collection of logical inference methods are all badly inadequate for this purpose.... Do you disagree? -- Ben G ------- To unsubscribe, change your address, or temporarily deactivate your subscription, please go to http://v2.listbox.com/member/
