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Artificial Intelligence Fundamentals Part 2
This deck covers key concepts in artificial intelligence, including probability, decision theory, game theory, and various AI methodologies and theories.
Probability
Besides logic and computation, the third great contribution of mathematics to AI is the theory of _________. The Italian Gerolamo Cardano (1501-1576) first framed the idea, describing it in terms of the possible outcomes of gambling events.
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Key Terms
Term
Definition
Probability
Besides logic and computation, the third great contribution of mathematics to AI is the theory of _________. The Italian Gerolamo Cardano (1501-1576) ...
Utility
The mathematical treatment of 'preferred outcomes' which was first formalized by Walras and was improved by Ramsey and later by von Neumann in his boo...
Decision Theory
A combination of probability theory with utility theory which provides a formal and complete framework for decisions (economic or otherwise) made unde...
Game
A scenario in which the actions of one player can significantly affect the utility of another (either positively or negatively).
Game Theory
The study of strategic decision making. Or 'the study of mathematical models of conflict and cooperation between intelligent rational decision-makers....
Operations Research
Coming from efforts in Britain to optimize radar installations, it is a discipline that deals with the application of analytical methods to help make ...
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| Term | Definition |
|---|---|
Probability | Besides logic and computation, the third great contribution of mathematics to AI is the theory of _________. The Italian Gerolamo Cardano (1501-1576) first framed the idea, describing it in terms of the possible outcomes of gambling events. |
Utility | The mathematical treatment of 'preferred outcomes' which was first formalized by Walras and was improved by Ramsey and later by von Neumann in his book The Theory of Games and Economic Behavior (1944). |
Decision Theory | A combination of probability theory with utility theory which provides a formal and complete framework for decisions (economic or otherwise) made under uncertainty. |
Game | A scenario in which the actions of one player can significantly affect the utility of another (either positively or negatively). |
Game Theory | The study of strategic decision making. Or 'the study of mathematical models of conflict and cooperation between intelligent rational decision-makers.' Unlike decision theory, it does not offer an unambiguous prescription for selecting actions. |
Operations Research | Coming from efforts in Britain to optimize radar installations, it is a discipline that deals with the application of analytical methods to help make better decisions. It later found civilian applications in complex management decisions. |
Markov Decision Processes (MDPs) | Provides a mathematical framework for modeling decision-making in situations where outcomes are partly random and partly under the control of a decision maker. |
Satisficing | A decision-making strategy that attempts to meet an acceptability threshold. This is contrasted with optimal decision-making, an approach that specifically attempts to find the best option available. |
Neuroscience | The study of the nervous system, particularly the brain. |
Neuron | An electrically excitable cell that processes and transmits information by electrical and chemical signaling |
Technological Singularity | The hypothetical future emergence of greater-than-human intelligence through technological means. Since the capabilities of such intelligence would be difficult for an unaided human mind to comprehend, the occurrence of a technological singularity is seen as an intellectual event horizon, beyond which events cannot be predicted or understood. |
Behaviorism | Rejected any theory involving mental processes on the grounds that introspection could not provide reliable evidence. |
Cognitive Psychology | A subdiscipline of psychology exploring internal mental processes. It is the study of how people perceive, remember, think, speak, and solve problems. |
Cognitive Science | The interdisciplinary scientific study of the mind and its processes. It examines what cognition is, what it does and how it works. |
Control Theory | An interdisciplinary branch of engineering and mathematics that deals with the behavior of dynamical systems. The external input of a system is called the reference. When one or more output variables of a system need to follow a certain reference over time, a controller manipulates the inputs to a system to obtain the desired effect on the output of the system. |
Homeostatic Devices | Ashby's Design for a Brain (1948, 1952) elaborated on his idea that intelligence could be created by the use of _____________ containing appropriate feedback loops to achieve stable adaptive behavior. |
Cybernetics | Wiener's book ___________(1948) became a bestseller and awoke the public to the possibility of artificially intelligent machines. |
Objective Function | Modern control theory, especially the branch known as stochastic optimal control, has as its goal the design of systems that maximize an ___________over time. |
Computational Linguistics | An interdisciplinary field dealing with the statistical or rule-based modeling of natural language from a computational perspective. |
Knowledge Representation (KR) | Involves analysis of how to reason accurately and effectively and how best to use a set of symbols to represent a set of facts within a knowledge domain. |
Hebb | The man who demonstrated a simple updating rule for modifying the connection strengths between neurons. |
Physical Symbol System | Takes physical patterns (symbols), combining them into structures (expressions) and manipulating them (using processes) to produce new expressions. |
Lisp | A family of computer programming languages with a long history and a distinctive, fully parenthesized Polish prefix notation. Has been the dominant AI programming language for the last 30 years. |
Microworlds | Minsky supervised a series of students who chose limited problems that appeared to require intelligence to solve. These limited domains became known as _________. |
Adaline (Adaptive Linear Neuron) | A single layer neural network. Consists of a weight, a bias and a summation function. |
Perceptron | An algorithm for supervised classification of an input into one of two possible outputs. It is a type of linear classifier, i.e. a classification algorithm that makes its predictions based on a linear predictor function combining a set of weights with the feature vector describing a given input. |
Perceptron Convergence Theorem | This theorem says that the learning algorithm can adjust the connection strengths of a perceptron to match any input data, provided such a match exists. |
Machine Evolution | The illusion of unlimited computational power was not confined to problem-solving programs. Early experiments in ____________ (now called genetic algorithms) (Friedberg, 1958; Friedberg et al., 1959) were based on the undoubtedly correct belief that by making an appropriate series of small mutations to a machine-code program, one can generate a program with good performance for any particular task. |
Genetic Algorithms | A search heuristic that mimics the process of natural evolution. This heuristic is routinely used to generate useful solutions to optimization and search problems. |
Weak Methods | Approaches which do not scale up to large or difficult problem instances. |