Simulating a human playing mastermind introducing noise in anti-mind algorithm

  • Authors:

  • José Barahona da Fonseca

  • Affiliations:

  • Department of Electrical Engineering and Computer Science, New University of Lisbon, Caparica, Portugal

  • Venue:
  • AIKED'06 Proceedings of the 5th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases
  • Year:
  • 2006

Quantified Score

Hi-index 0.00

Visualization

Abstract

When I started my career, in 1986, my main research interests were Artificial Intelligence(AI), Expert Systems and Decision Support Systems based on AI tools. The first experiment that I have done was the development of the Anti-Mind and Master Mind with Feedback programs written in the Basic language [1]. The Anti-Mind program simulates a good player of the Master Mind game, discovering the secret code defined by the human operator (a sequence of numbers in a pre-defined interval) very quickly. Then I used the algorithm of Anti-Mind to help and correct a human operator trying to discover the secret code defined by the computer resulting in the Master Mind with Feedback. Let's take an example to clarify what I mean by the 'Computer Thinks better than the human' and seems to have a higher IQ: Anti-Mind Program CPC=Number of Correct Digits in Correct Position CPE=Number of Correct Digits in Incorrect Position 3 Digits Interval [0,3] 1. 103 CPC,CPE=1,1 2. 132 CPC,CPE=1,1 3. 120 CPC,CPE=0,2 **Enough Information!** Secret code=? The computer knows that the information is enough and it also knows the secret code. And you? In this paper I will present the algorithms of Anti-Mind and Mastermind with Feedback with some worked examples and I will discuss, at the light of Cognitive Science, why is the computer a better player than the best human Mastermind players. Finally I will try to simulate a human player and his cognitive limitations introducing noise in the good move chosen randomly from the good moves. In the near future I am planning to introduce logical processing limitations simulated by discarding some previous moves and/or limiting the number of previous moves considered in the generation of the good moves coherent with them from which will be selected and altered the final move.