Inductive Logic Programming

Tools developers face the challenge of exposing a development methodology to users while concealing details of the underlying system. If that system is complex and subject to evolution, this problem can be particularly difficult. Here, we discuss the use of software ...

Traditional statistical machine learning approaches assume: – A random sample of homogeneous objects from single relation – Independent, identically distributed (IID) Traditional relational machine learning approaches assume: – Logical language for describing structure in sample – No noise and no uncertainty Real world data sets: – Multi Multi‐relational relational and heterogeneous – Noisy and uncertainWeb & Query Logs The InternetQuery Log Relationships Q b Q a Clicked-For Shares-Terms

The objective of this paper is to review the state-of-the-art of statistical relational learning (SRL) models developed to deal with machine learning and data mining in relational domains in presence of missing, partially observed, and/or noisy data. It starts by giving a general overview of conventional graphical models, first-order logic and inductive logic programming approaches as needed for background. The historical development of each SRL key model is critically reviewed. The study also focuses on the practical application of SRL techniques to a broad variety of areas and their limitations.

Inducing concept descriptions from examples has been thoroughly tackled by symbolic machine learning methods. However, on-line learning methods that acquire concepts from examples distributed over time, require great computational effort. This is not only due to the intrinsic complexity of the concept learning task, but also to the full memory approach that most learning systems adopt. Indeed, during learning, most of these systems consider all their past examples leading to expensive procedures for consistency verification. In this paper, we present an implementation of a partial memory approach through an advanced data storage framework and show through experiments that great savings in learning times can be achieved. We also propose and experiment different ways to select the past examples paving the way for further research in on-line partial memory learning agents.

Chatbots are tools aimed at simplifying the interaction between humans and computers, typically used in dialogue systems for various practical purposes. These systems should be built on ethical foundations because their behavior may heavily influence a user (think especially about children). The primary objective of this paper is to present the architecture and prototype implementation of a Multi Agent System (MAS) designed for ethical monitoring and evaluation of a dialogue system. A prototype application, for monitoring and evaluation of chatting agents’ (human/artificial) ethical behavior in an online customer service chat point w.r.t their institution/company’s codes of ethics and conduct, is developed and presented. We focus on the implementation specifics of the proposed system and the presented prototype application. Future work and open issues with this research are discussed.

Autonomous intelligent agents are increasingly engaging in human communities. Thus, they must be expected to follow social and ethical norms of the community in which they are deployed in. In this work we present an approach for developing such ethical agents which are able to develop ethical decision making and judgment capabilities by learning from interactions with the users. Our approach is a logic-based approach and the resulting ethical agents are transparent by design.

Teaching programming to the amateur programmers and non-programmers with design and educational technology background are tough. Needless to say, it is pertinent for them to have some basic programming skills. We realized the needs to motivate the potential programmers therefore, proposed the integration of folktales in learning programming. This paper shares an approach of teaching programming using the folktales to create awareness also to simulate the amateur and non-programmers interested with folktales at the same time motivated to complete a creation of digital story using 3D programming software, Alice and Unity. Mixed-methods approach revealed participants’ positive behavior towards learning programming although it was perceived difficult. The finding also indicates that they can understand the programming language and successfully explored the software to create a digital story.

Teaching programming to the amateur programmers and non-programmers with design and educational technology background are tough. Needless to say, it is pertinent for them to have some basic programming skills. We realized the needs to motivate the potential programmers therefore, proposed the integration of folktales in learning programming. This paper shares an approach of teaching programming using the folktales to create awareness also to simulate the amateur and non-programmers interested with folktales at the same time motivated to complete a creation of digital story using 3D programming software, Alice and Unity. Mixed-methods approach revealed participants’ positive behavior towards learning programming although it was perceived difficult. The finding also indicates that they can understand the programming language and successfully explored the software to create a digital story.

This paper proposes a machine learning approach dealing with genetic programming to build classifiers through logical rule induction. In this context, we define and test a set of mutation operators across from different clinical datasets to improve the performance of the proposal for each dataset. The use of genetic programming for rule induction has generated interesting results in machine learning problems. Hence, genetic programming represents a flexible and powerful evolutionary technique for automatic generation of classifiers. Since logical rules disclose knowledge from the analyzed data, we use such knowledge to interpret the results and filter the most important features from clinical data as a process of knowledge discovery. The ultimate goal of this proposal is to provide the experts in the data domain with prior knowledge (as a guide) about the structure of the data and the rules found for each class, especially to track dichotomies and inequality. The results reached by ...

Abstract: Motivated by a novel application of description logics in the area ofelectronic commerce, this paper investigates a new instance of the problemof rewriting concepts using terminologies, namely the best covering problem:given a concept description Q and a terminology T , the problem consists innding a rewriting of Q that uses only concept names from T and containsas much as