AI Practical Work 2

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Across
  1. 2. Uninformed search algorithm that aims to find a path to the goal node which has the lowest cumulative cost.
  2. 4. Biological correspondent of the weights in an artificial neural network.
  3. 5. Logic programming language associated with artificial intelligence and computational linguistics.
  4. 6. Finite sequence of well-defined computer instructions to solve a computation problem.
  5. 8. Computer program developed by DeepMind that uses machine learning (artificial neural networks) to identify the best moves to win the board game “Go”.
  6. 11. Elementary unit of an artificial neural network.
  7. 12. Restricted Boltzmann Machine: generative stochastic artificial neural network that can learn a probability distribution over its set of inputs.
  8. 13. Interdisciplinary subfield of computer science and computational linguistics that develops methodologies and technologies that enable the recognition and translation of spoken language into text by computers.
  9. 14. Interdisciplinary branch of computer science and engineering. It involves design, construction, operation, and use of robots and has as its goal to design machines that can help and assist humans.
  10. 16. Realistic humanoid robot designed for research, education, and entertainment, and helps promote public discussion about Artificial Intelligence ethics and the future of robotics. Developed by the Hong Kong-based company Hanson Robotics.
  11. 17. Branch of mathematics used to model the strategic interaction between different players in a context with predefined rules and outcomes.
  12. 22. IBM’s question-answering computer system that is capable of answering questions posed in natural language.
  13. 24. Type of machine learning and artificial intelligence that imitates the way humans gain certain types of knowledge.
  14. 25. Type of artificial intelligence that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so.
  15. 26. Algorithm for supervised learning of artificial neural networks using gradient descent.
Down
  1. 1. Computerized system composed of multiple interacting intelligent agents.
  2. 3. Computing systems that are based on a collection of nodes that simulate the neurons in the human brain.
  3. 7. Method of inquiry in artificial intelligence for determining whether or not a computer is capable of thinking like a human being.
  4. 9. simulation of human intelligence processes by machines, especially computer systems.
  5. 10. act of defining a problem; determining the cause of the problem; identifying, prioritizing, and selecting alternatives for a solution; and implementing a solution.
  6. 15. Algorithm for supervised learning of binary classifiers.
  7. 18. Learning rule that describes how the neuronal activities influence the connection between neurons.
  8. 19. Artificial Immune Systems: machine learning systems inspired by theoretical immunology.
  9. 20. Java Agent Development Framework: software framework for the development of an intelligent agent, implemented in Java.
  10. 21. Fully connected network with each node connecting to every other node, including itself. The nodes compete against each other by sending out inhibiting signals to each other.
  11. 23. Adaptive resonance theory: describes a number of neural network models which use supervised and unsupervised learning methods, and address problems such as pattern recognition and prediction.