23. Model Merging, Cross-Modal Coupling, Course Summary

Bayesian Story Merging


Cross-Modal Coupling


Applications of AI

  • Scientific approach: understanding how AI works
  • Engineering approach: implementing AI applications



Genesis system



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 for a given set of events. To know which model is right, the Bayesian probabilities formulas can be used to confirm if events are independent or not, make them easier to compute, and choose the more appropriate model.

  • P(a/b) P(b) = P(a,b) = P(b/a) P(a)
  • P(a/b) = P(b/a) P(a) / P(b)

Defining a as a class, and b as the evidence, the probability of the evidence given the class can be obtained through these formulas.

P(class/evidence) = P(evidence/class) P(class) / P(evidence)

Using the evidence from experience, classes can inferred by analyzing the results and corresponding probabilities.

Structure discovery

Given the data from experience / simulation, the right model can be sorted as it better corresponds to the probabilities. This allows to select between 2 existing models.

However if multiple models can be created, volumes of data make it impossible to compare them all. The solution is to use two models and compare them recursively. At each trial, the losing model is modified for improvements until a model fits certain criteria for success.

A trick is to use the sum of the logarithms rather than the probabilities, as large numbers of trials will make numbers too small to compute properly.

To avoid local maxima, a radical rearrangement of structure is launched after a certain number of trials.


This Bayesian structure discovery works quite well in situations when a diagnosis must be completed: medical diagnosis, lie-detector, symptoms of aircraft or program not working…

21. Probabilistic Inference I

Probabilities in Artificial Intelligence



Reminders of probabilities formulas

Basic axioms of probability

  • 0 ≤ P(a) ≤  1

Basic definitions of probability


Chain rule of probability


Chain rule of probability


Independent events


Conditional independence

If a and b are independent


Belief nets


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:

  1. Long-term memory and short-term memory
  2. Assertions and rules (production)
  3. Preferences systems between the rules
  4. Problem spaces (make a space and search through that space)
  5. Universal sub-goaling: new problems that emerge during the resolution become entire new goal with assertions rules, etc.

SOAR relies on the symbol system hypothesis. It primarily deals with deliberative thinking.

Emotion machine

Created by Marvin Minsky to tackle more complex problems, this architecture involves thinking about several layers:

  • Reflective thinking
    • Self-conscious
    • Self-reflective
  • Deliberative thinking
  • Learned reaction
  • Instinctive reaction

It is based upon the common sense hypothesis.


System created by Rodney Brooks. By generalizing layers of abstraction in the building of robots (such as for robot vision and movement), modifications to certain layers don’t interfere with other layers computation, allowing for better incremental improvement of the system as a whole.

It primarily deals with instinctive reaction and learned reaction.

This is the creature hypothesis, if a machine can act as an insect, then it will be easy to develop further later. This architecture relies upon the following principles:

  1. No representation
  2. Use the world instead of model: reacting to the world constantly
  3. Finite state machines


Based upon language, this system involves perception and description of events, which then allow to understand stories and further, culture both at the macro (country, religion…) and micro (family…) levels. This system relies upon the strong story hypothesis.

18. Representations: Classes, Trajectories, Transitions



  • Combinators: linking objects together
  • Reification: actions implying results
  • Localization: a frame where objects and actions happen
  • Story sequence: a series of actions happening linearly in time






Language also tracks movement in the description of actions.



Story sequences


Story libraries
