Category: Theory
24. Reviews and Exercises
In this post are gathered the tutorials and exercises, called “mega-recitations”, to apply the concepts presented in the Artificial Intelligence course of Prof. Patrick Winston.
23. Model Merging, Cross-Modal Coupling, Course Summary
Bayesian Story Merging By using the probability model discovery previously studied, certain concepts and ideas can be analyzed and merged if similar. Cross-Modal Coupling By analyzing the correspondences between clusters of two sets of data, certain data subsets regular correspondences can be sorted out. According to Prof. Patrick Winston, this system of correspondences is very…
22. Probabilistic Inference II
Beliefs nets Continued from previous class Events diagrams must always be arranged in a way so that there are final nodes and no loops. Recording probabilities in tables for each event, the tables are filled by repeating experience so as to know the probabilities and occurrences of each event. Bayesian inference Several models can be drawn…
21. Probabilistic Inference I
Probabilities in Artificial Intelligence With a joint probability table, recording the tally of crossed events occurrence will allow us to measure the probabilities of each event happening, conditional or unconditional probabilities, independence of events, etc. The problem with such table is that as the number of variables increase, the number of rows in the table…
19. Architectures: GPS, SOAR, Subsumption, Society of Mind
General Problem Solver By analyzing the difference between a current state and desired state, a set of intermediary steps can be created to solve the problem = problem solving hypothesis. SOAR (State Operator And Result) SOAR Components: Long-term memory Short-term memory Vision system Action system Key parts of the SOAR architecture: Long-term memory and short-term…