Tuesday 25th September, 2012
1:00pm to 1:50pm
Guessing is a part of life and science. We form a hypothesis, work out the consequences and compare with observations. Lots of problems are formulated by first assuming that the solution exists and then describing the properties it should have. Planning, scheduling, diagnostic, learning problems and Sudoku all follow this pattern. Guessing is good not only for describing these problems but also for solving them. We make a guess — often a series of guesses — to build, for example, a schedule, and check if it satisfies resource, timeliness and other constraints. Often, we guess again.
How do we write “guess the value of this variable” in code? How do we code “guess again”? How to put in prior knowledge favoring some guesses? The talk first will answer these questions.
Naive guessing however is hopeless even for toy problems. We often have to make lots of guesses before we build a candidate solution to check against the constraints. Only a tiny or even infinitesimal proportion of these guesses yield a successful candidate. How to make good guesses? That is very hard to know: Most physical, biological, sociological, etc. models are set up to compute consequences of the causes rather than the other way around. It helps to reformulate the question: how to avoid too many bad guesses? The talk will describe and illustrate a general principle, found in any serious logic, non-deterministic or probabilistic programming system.
The techniques explained in the talk are not tied to any language and can be used even in C. However, functional languages, especially typed ones, have a serious advantage. No prior knowledge of logic or non-deterministic programming is required. The ability to read introductory OCaml or Haskell code will be helpful. The participants will learn how to guess in their favorite language, and what it takes to succeed doing so. They will see laziness, unification and constraint propagation in the same light.
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