OPUS is a branch and bound search algorithm that enables efficient admissible search through spaces for which the order of search operator application is not significant. The algorithm's search efficiency is demonstrated with respect to very large machine learning search spaces. The use of admissible search is of potential value to the machine learning community as it means that the exact learning biases to be employed for complex learning tasks can be precisely specified and manipulated. OPUS also has potential for application in other areas of artificial intelligence, notably, truth maintenance.

A tight integration of Mitchell's version space algorithm with Agrawal et al.'s Apriori algorithm is presented. The algorithm can be used to generate patterns that satisfy a variety of constraints on data. Constraints that can be impoesed on...

: This paper presents a novel learning model Clarion, which is a hybrid model based on the twolevel approach proposed in Sun (1995). The model integrates neural, reinforcement, and symbolic learning methods to perform on-line, bottom-up learning (i.e., learning that goes from neural to symbolic representations). The model utilizes both procedural and declarative knowledge (in neural and symbolic representations respectively), tapping into the synergy of the two types of processes. It was applied to deal with sequential decision tasks. Experiments and analyses in various ways are reported that shed light on the advantages of the model. obstacles agent target Figure 1: Navigating Through A Minefield 1 Introduction This paper presents a model that unifies neural, symbolic, and reinforcement learning. It addresses the following three issues: (1) It deals with autonomous learning: It allows a situated agent to learn autonomously and continuously, from on-going experience in the world, w...

We introduce the boolean inductive query evaluation problem, which is concerned with answering inductive queries that are arbitrary boolean expressions over monotonic and anti-monotonic predicates. Secondly, we develop a decomposition theory for inductive query evaluation in which a boolean query Q is reformulated into k sub-queries Q i = QA ^ QM that are the conjunction of a monotonic and an anti-monotonic predicate. The solution to each subquery can be represented using a version space. We investigate how the number of version spaces k needed to answer the query can be minimized. Thirdly, for the pattern domain of strings, we show how the version spaces can be represented using a novel data structure, called the version space tree, and can be computed using a variant of the famous Apriori algorithm. Finally, we present some experiments that validate the approach.

For dealing with reactive sequential decision tasks, a learning model Clarion was developed, which is a hybrid connectionist model consisting of both localist (symbolic) and distributed representations, based on the two-level approach proposed in Sun (1995). The model learns and utilizes procedural and declarative knowledge, tapping into the synergy of the two types of processes. It unifies neural, reinforcement, and symbolic methods to perform on-line, bottom-up learning (from subsymbolic to symbolic knowledge). Experiments in various situations shed light on the working of the model. Its theoretical implications in terms of symbol grounding are also discussed.

A great challenge in using any planning system to solve real-world problems is the difficulty of acquiring the domain knowledge that the system will need. We present a way to address part of this problem, in the context of Hierarchical Task Network (HTN) planning, by having the planning system incrementally learn conditions for HTN methods under expert supervision. We present a general formal framework for learning HTN methods, and a supervised learning algorithm, named CaMeL, based on this formalism. We present theoretical results about CaMeL’s soundness, completeness, and convergence properties. We also report experimental results about its speed of convergence under different conditions. The experimental results suggest that CaMeL has the potential to be useful in real-world applications.

Abstract. Inductive databases (IDBs) have been proposed to afford the problem of knowledge discovery from huge databases. With an IDB the user/analyst performs a set of very different operations on data using a query language, powerful enough to perform all the required elaborations, such as data preprocessing, pattern discovery and pattern postprocessing. We present a synthetic view on important concepts that have been studied within the cInQ European project when considering the pattern domain of itemsets. Mining itemsets has been proved useful not only for association rule mining but also feature construction, classification, clustering, etc. We introduce the concepts of pattern domain, evaluation functions, primitive constraints, inductive queries and solvers for itemsets. We focus on simple high-level definitions that enable to forget about technical details that the interested reader will find, among others, in cInQ publications. 1

The concept of jumping emerging patterns (JEPs) has been proposed to describe those discriminating features which only occur in the positive training instances but do not occur in the negative class at all; JEPs have been used to construct classifiers which generally provide better accuracy than the state-of-the-art classifiers such as C4.5. The algorithms for maintaining the space of jumping emerging patterns (JEP space) are presented in this paper. We prove that JEP spaces satisfy the property of convexity. Therefore JEP spaces can be concisely represented by two bounds: consisting respectively of the most general elements and the most specific elements. In response to insertion of new training instances, a JEP space is modified by operating on its boundary elements and the boundary elements of the JEP spaces associated with the new instances. This strategy completely avoids the need to go back to the most initial step to build the new JEP space. In addition, our ...

Patterns of contrast are a very important way of comparing multidimensional datasets. Such patterns are able to capture regions of high difference between two classes of data, and are useful for human experts and the construction of classifiers. However, mining such patterns is particularly challenging when the number of dimensions is large. This paper describes a new technique for mining several varieties of contrast pattern, based on the use of Zero-Suppressed Binary Decision Diagrams (ZBDDs), a powerful data structure for manipulating sparse data. We study the mining of both simple contrast patterns, such as emerging patterns, and more novel and complex contrasts, which we call disjunctive emerging patterns. A performance study demonstrates our ZBDD technique is highly scalable, substantially improves on state of the art mining for emerging patterns and can be effective for discovering complex contrasts from datasets with thousands of attributes.

To deal with sequential decision tasks, we present a learning model Clarion, which is a hybrid connectionist model consisting of both localist and distributed representations, based on the two-level approach proposed in Sun (1995). The model learns and utilizes procedural and declarative knowledge, tapping into the synergy of the two types of processes. It unifies neural, reinforcement, and symbolic methods to perform on-line, bottom-up learning. Experiments in various situations are reported that shed light on the working of the model. 1 Introduction This paper presents a hybrid model that unifies neural, symbolic, and reinforcement learning into an integrated architecture. It addresses the following three issues: (1) It deals with concurrent on-line learning: It allows a situated agent to learn continuously from on-going experience in the world, without the use of preconstructed data sets or preconceived concepts. (2) The model learns not only low-level procedural skills but also hi...