Broken Glass Summaries Steps towards Artificial Intelligence- marvin Minsky

Heuristic programming is a method of incorporating intelligence into machines. Heuristics refer to “rules of thumb” that increase the efficiency of a machine in solving a complex problem, a heuristic may therefore fail in certain cases but overall can be assumed to reduce the complexity of finding the solution to problem considerably.

1. 
   Search: This refers to the technique of searching a solution space for a suitable solution to a problem. This assumes that the problem is well defined in the sense that we can decide when a solution found out is acceptable. All these problems can be theoretically solved by simply exhausting all possibilities, but generally this method is inefficient for practical purposes. We can reduce the complexity of the search by techniques such as Hill climbing. In Hill climbing at attempt is made to maximize the output function by adjusting the input parameters. To do so we need to explore locally about a point and try to find out which direction in the space leads to the steepest uphill climb. This still suffers from many problems for instance if we reach a local maxima then we might incorrectly decide that it is the optimal solution. Another more serious problem is that of the “Mesa Phenomenon” in which locally moving in any direction leads to no performance improvement.
   
2. 
   Pattern Recognition: This deals with the classification of problems into classes. Within a class a particular method of problem solving is most applicable. This acts as the defining feature for that class. For this the Heuristic features of the problem have to be identified and the method most suited for them has to be figured out.
   
3. 
   Learning: The basic goal of this area is to apply similar methods to a particular problem that have worked on similar problems in the past. One method to achieve this is using success-reinforced models wherein success in the application of a particular method leads to a reward that increases the probability of that particular method to be used again in problems of a similar nature. For complex problems we can divide them into smaller problems each with local goals and methods taken to reach these goals can be reinforced.
   
4. 
   Planning: While solving a problem we may be faced with a large number of sub problems. At each stage of problem solving a subset of problems has to be selected based on the relative difficulty and appropriate methods have to be selected to solve them. In cases of difficult problems the steps taken to search for selecting suitable sub problems and appropriate methods for them may become too difficult therefore we must look at the problem as a whole and analyze its structure, this is termed as planning.
   
5. 
   Induction: This final area explores what intelligence actually defines. According to Minsky any machine that can generate a set structures (by learning) to solve a problem, that are not anticipated by the programmer can be said to be intelligent. Inductive inference deals with how machines can generate general explanations about events that are totally different from what it has ‘learned’ from experience. If a system can answer a hypothetical question without performing an experiment then it must have created an internal model of the world to represent that event. This internal model must represent the machine also thus creating self models that aid in introspection. Minsky believes that such self models will exhibit a similar kind of duality that human’s exhibit.
   

Turing attempts to answer the question “Can machines think?” He transforms the question using an “imitation game”.

This game is played among three people a man (A), a woman (B) and an interrogator (C). The Interrogator is apart from the other two. The object of the game is for the interrogator to identify which is the man and which is the woman in the end of the game. To find this out the interrogator is allowed to pose questions to both. The object of A is to mislead the interrogator (C) and try and cause him to make the wrong identification. The object of B is to help the interrogator (C) in making the right decision.

To explain his question transformation Turing assumes that a machine takes the part of A. He now poses the questions “Will the interrogator decide wrongly as often when the game is played like this as he does when the game is played between a man and a woman?” these questions replace the question “Can machines think?”.