SINERGY: A linear planner based on genetic programming

Abstract. In this paper we describe SINERGY, which is a highly parallelizable, linear planning system that is based on the genetic programming paradigm. Rather than reasoning about the world it is planning for, SINERGY uses artificial selection, recombination and fitness measure to generate linear plans that solve conjunctive goals. We ran SINERGY on several domains (e.g., the briefcase problem and a few variants of the robot navigation problem), and the experimental results show that our planner is capable of handling problem instances that are one to two orders of magnitude larger than the ones solved by UCPOP. In order to facilitate the search reduction and to enhance the expressive power of SINERGY, we also propose two major extensions to our planning system: a formalism for using hierarchical planning operators, and a framework for planning in dynamic environments.

Extraction Patterns for Information Extraction Tasks: A Survey

Information Extraction systems rely on a set of extraction patterns that they use in order to retrieve from each document the relevant information. In this paper we survey the various types of extraction patterns that are generated by machine learning algorithms. We identify three main categories of patterns, which cover a variety of application domains, and we compare and contrast the patterns from each category

The Very Offline k-Vehicle Routing Problem in Trees

The vehicle routing problem (VRP) in trees is a restriction of the general vehicle routing problem in which the underlying graph is a tree. In this paper, we introduce several NP-complete variants of the problem (e.g., common-source, common-destinations, in-place, flexiblerequests, and bring-it-back), and we present five fast approximation algorithms for these variants. We mainly focus on the 2-vehicle routing problems, but we also consider possible generalizations for the k-vehicle routing case. In our analysis, the underlying graphs consist of various types of trees: binary and non-binary, weighted and unweighted. 1

A General-Purpose Ai Planning System Based On The Genetic Programming Paradigm

In this paper we describe SYNERGY, which is a general-purpose AI planning system that is based on the genetic programming paradigm. Rather than reasoning about the planning world, SYNERGY uses selection, mutation, recombination and fitness measure to generate linear plans that solve conjunctive goals. We ran SYNERGY on several domains, and the experimental results show that our planner solves problem instances that are up to two orders of magnitude larger than the ones solved by UCPOP. KEYWORDS: AI planning, genetic programming, conjunctive goals INTRODUCTION Artificial intelligence (AI) planning is known to be an extremely hard problem (see [2]), and it is generally accepted that most non-trivial planning problems are at least NPcomplete. In order to cope with the combinatorial explosion of the search problem, AI researchers proposed a wide variety of solutions, from search control rules [3] to hierarchical planning [8] to skeletal planning [5]. More recently, we witnessed the occur..

Adaptive View Validation: A First Step Towards Automatic View Detection

Multi-view algorithms reduce the amount of required training data by partitioning the domain features into separate subsets or views that are sufficient to learn the target concept. Such algorithms rely on the assumption that the views are sufficiently compatible for multi-view learning (i.e., most examples are labeled identically in all views). In practice, it is unclear whether or not two views are sufficiently compatible for solving a new, unseen learning task. In order to cope with this problem, we introduce a view validation algorithm: given a learning task, the algorithm predicts whether or not the views are sufficiently compatible for solving that particular task. We use information acquired while solving several exemplar learning tasks to train a classifier that discriminates between the tasks for which the views are sufficiently and insufficiently compatible for multi-view learning. Our experiments on wrapper induction and text classification show that view validation requires only a modest amount of training data to make high accuracy predictions

Active Learning with Multiple Views

Inductive learning algorithms typically use a set of labeled examples to learn class descriptions for a set of user-specified concepts of interest. In practice, labeling th

A Hierarchical Approach to Wrapper Induction

With the tremendous amount of information that becomes available on the Web on a daily basis, the ability to quickly develop information agents has become a crucial problem. A vital component of any Web-based information agent is a set of wrappers that can extract the relevant data from semistructured information sources. Our novel approach to wrapper induction is based on the idea of hierarchical information extraction, which turns the hard problem of extracting data from an arbitrarily complex document into a series of easier extraction tasks. We introduce an inductive algorithm, stalker, that generates high accuracy extraction rules based on user-labeled training examples. Labeling the training data represents the major bottleneck in using wrapper induction techniques, and our experimental results show that stalker does significantly better then other approaches; on one hand, stalker requires up to two orders of magnitude fewer examples than other algorithms, while on the other hand..