Stochastic Local Search in k-Term DNF Learning
نویسندگان
چکیده
A novel native stochastic local search algorithm for solving k-term DNF problems is presented. It is evaluated on hard k-term DNF problems that lie on the phase transition and compared to the performance of GSAT and WalkSAT type algorithms on SAT encodings of k-term DNF problems. We also evaluate state-of-the-art separate and conquer algorithms on these problems. Finally, we demonstrate the practical relevance of our algorithm on a chess endgame database.
منابع مشابه
Machine Learning in the Phase Transition Framework
We investigate k-term DNF learning, the task of inducing a propositional DNF formula with at most k terms from a set of positive and negative examples. Though k-term DNF learning is NP-complete, it is at the core of most propositional learning algorithms. The main aim of this thesis is to evaluate stochastic local search algorithms to solve hard k-term DNF learning tasks. As a reference we firs...
متن کاملPhase Transitions and Stochastic Local Search in k-Term DNF Learning
In the past decade, there has been a lot of interest in phase transitions within artificial intelligence, and more recently, in machine learning and inductive logic programming. We investigate phase transitions in learning k-term DNF boolean formulae, a practically relevant class of concepts. We do not only show that there exist phase transitions, but also characterize and locate these phase tr...
متن کاملILP Through Propositionalization and Stochastic k-Term DNF Learning
ILP has been successfully applied to a variety of tasks. Nevertheless, ILP systems have huge time and storage requirements, owing to a large search space of possible clauses. Therefore, clever search strategies are needed. One promising family of search strategies is that of stochastic local search methods. These methods have been successfully applied to propositional tasks, such as satisfiabil...
متن کاملLearning Non-Deterministic Multi-Agent Planning Domains
In this paper, we present an algorithm for learning nondeterministic multi-agent planning domains from execution examples. The algorithm uses a master-slave decomposition of two population-based stochastic local search algorithms and integrates binary decision diagrams to reduce the size of the search space. Our experimental results show that the learner has high convergence rates due to an agg...
متن کاملLearning Nearly Monotone k-term DNF
This note studies the learnability of the class k-term DNF with a bounded number of negations per term. We study the case of learning with membership queries alone, and give tight upper and lower bounds on the number of negations that makes the learning task feasible. We also prove a negative result for equivalence queries. Finally, we show that a slight modiication in our algorithm proves that...
متن کامل