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So you're building a program that can learn to write datalog programs. Is it because datalog programs are tedious enough they should be automateable? Why this p

by iamwil 7y ago

So you're building a program that can learn to write datalog programs. Is it because datalog programs are tedious enough they should be automateable? Why this particular research direction? > The space of hypotheses doubles in size when negation as failure is allowed, which improves the efficiency of the search for hypotheses. That line confused me. Shouldn't a double in the search space decrease the efficiency of the search for the hypothesis?

srean 7y ago
Whole lot easier to understand, compose and guide than DNNs. Thats a reason enough.
- YeGoblynQueenne 7y ago
  Well, there's that. My intuition is that ILP should appeal to computer scientists more than statistical machine learning. Unfortunately, it is not very widely known- we're a small field.
  - srean 7y ago
    Lets drink to its health/resurgence. I am an more on the applied maths camp rather than the CS camp but I feel a practical and effective merger of logic and probabilistic reasoning is sorely needed to climb out of the rut we are in. Neural Turing is all nice and dandy but it seems a very heavy handed way of expressing/learning dependencies.
YeGoblynQueenne 7y ago
>> Why this particular research direction? The work I described falls under the category of Inductive Logic Programming, a field at the intersection of machine learning and logic programming. In ILP (as in logic programming) it's typical to restrict the expressivity of first-order languages to improve efficiency and to guarantee termination. Such restrictions often apply primarily to theoretical results, while in practice there is more freedom. For example, my group's algorithms can learn normal programs in practice, but our theoretical results only guarantee termination (and so, learnability) of definite datalog programs. In general, it takes a lot of work to find a representation language that is expressive enough to allow learing of interesting and useful programs, and at the same time, restricted enough to guarantee those programs _can_ be learned. >> The space of hypotheses doubles in size when negation as failure is allowed, which improves the efficiency of the search for hypotheses I'm sorry if this was confusing. I meant that restricting hypotheses to definite programs improves efficiency compared to allowing normal programs (with negation as failure). The reason is that, if negation as failure is allowed, in the worst case, an algorithm must consider both a literal and its negation for inclusion in a clause, which doubles the cost of search for a hypothesis.

by iamwil 7y ago

srean 7y ago

Whole lot easier to understand, compose and guide than DNNs. Thats a reason enough.

YeGoblynQueenne 7y ago
Well, there's that. My intuition is that ILP should appeal to computer scientists more than statistical machine learning. Unfortunately, it is not very widely known- we're a small field.
- srean 7y ago
  Lets drink to its health/resurgence. I am an more on the applied maths camp rather than the CS camp but I feel a practical and effective merger of logic and probabilistic reasoning is sorely needed to climb out of the rut we are in. Neural Turing is all nice and dandy but it seems a very heavy handed way of expressing/learning dependencies.

YeGoblynQueenne 7y ago

>> Why this particular research direction? The work I described falls under the category of Inductive Logic Programming, a field at the intersection of machine learning and logic programming. In ILP (as in logic programming) it's typical to restrict the expressivity of first-order languages to improve efficiency and to guarantee termination. Such restrictions often apply primarily to theoretical results, while in practice there is more freedom. For example, my group's algorithms can learn normal programs in practice, but our theoretical results only guarantee termination (and so, learnability) of definite datalog programs. In general, it takes a lot of work to find a representation language that is expressive enough to allow learing of interesting and useful programs, and at the same time, restricted enough to guarantee those programs _can_ be learned. >> The space of hypotheses doubles in size when negation as failure is allowed, which improves the efficiency of the search for hypotheses I'm sorry if this was confusing. I meant that restricting hypotheses to definite programs improves efficiency compared to allowing normal programs (with negation as failure). The reason is that, if negation as failure is allowed, in the worst case, an algorithm must consider both a literal and its negation for inclusion in a clause, which doubles the cost of search for a hypothesis.