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Bridging Artificial Intelligence, Psychometrics, and Economics: New Theory of Intelligence & Rationality Selmer Bringsjord and Bettina Schimanski “A Science Based Approach to Decision-Making” Co-sponsored by the Dept. of Economics and the Dept. of Cognitive Science Friday, November 21, 2003
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Selmer Bringsjord and Bettina Schimanski
“A Science Based Approach to Decision-Making”
Co-sponsored by the Dept. of Economics and
the Dept. of Cognitive Science
Friday, November 21, 2003
An Answer To: What is AI?
Wechsler Adult Intelligent Scale
WAIS includes many sub-tests
- PERI has already cracked this
- Deals with many mental facets
PERI should specifically solve all of the WAIS
- From there, we move to all other established tests
Ex: “Why are the tires of automobiles made of rubber, rather than, say, plastic?”
The agent receives percepts from the different environments and generates a behavior or result that in turn causes the environment to generate a state history. The performance measure evaluates the state history to arrive at the value of the agent.
Figure from Stuart Russell’s Rationality and Intelligence ’02.
The capacity to generate maximally successful behavior given the available information.
Fundamental inputs include:
E = the environment class in which the agent is to operate
U = the performance measure
V(f,E,U) = the expected value according to U obtained by an agent function f in environment class E.
Then a perfectly rational agent is defined by an agent function fopt such that:
This is just a fancy way of saying that the best agent does the best it can.
The in-principle capacity to compute the perfectly rational decision given the initially available information.
Calculative rationality is displayed by programs that, if executed infinitely fast, would result in perfectly rational behavior.
Unlike perfect rationality, calculative rationality is a requirement that can be fulfilled by many real programs.
On the other hand, calculative rationality is not necessarily a desirable property.
The capacity to generate maximally successful behavior given the available information and computational resources.
Fundamental Inputs include:
Agent(p,M) = the agent function implemented by the program p running on machine M
PM = finite set of all programs that can be run on M
Therefore the bounded optimal program popt is defined by:
Simply put, this is the notion of finding the program that generates the optimal feasible solution given the environment class E and performance measure U.
Compare describing the running time of algorithms in terms of the O( ) notation (i.e. Big O). This provides a way to describe the complexity independent of machine speeds and implementation details. For this reason we investigate Asymptotic Bounded Rationality.
where kM denotes a version of M speeded up by a factor of k (or with k times more memory) and V*(f, E, U, n) is the minimum value of V(f, E, U) for all e E of complexity n.
This means that the program is basically along the right lines if it just needs a better (i.e. faster or larger) machine to have worst-case behavior as good as that of any other program in all environments.
Environment classes correspond to those for each sub-test.
The performance measure is the grading of the results of the test.
The utility is the score.
Top loop of diagram corresponds to agent taking the test.
Advanced Synthetic Characters