Constraints on predicate invention

This chapter describes an inductive learning method that derives logic programs and invents predicates when needed. The basic idea is to form the least common anti-instance (LCA) of selected seed examples. If the LCA is too general it forms the starting poínt of a gneral-to-specific search which is guided by various constraints on argument dependencies and critical terms. A distinguishing feature of the method is its ability to introduce new predicates. Predicate invention involves three steps. First, the need for a new predicate is discovered and the arguments of the new predicate are determíned using the same constraints that guide the search. In the second step, instances of the new predicate are abductively inferred. These instances form the input for the last step where the definition of the new predicate is induced by recursively applying the method again. We also outline how such a system could be more tightly integrated with an abductive learning system

Predicate Invention in Inductive Logic Programming

The ability to recognise new concepts and incorporate them into our knowledge is an essential part of learning. From new scientific concepts to the words that are used in everyday conversation, they all must have at some point in the past, been invented and their definition defined. In this position paper, we discuss how a general framework for predicate invention could be made, by reasoning about the problem at the meta-level using an appropriate notion of top theory in inductive logic programming

Learning Complex Recursive Rules.

We consider the problem of learning complex recursive rules which involve new concepts other than those given in the input relations and which must be discovered by the learning algorithm in the course of finding rules. The existing learning methods (FOIL, FORGE, and etc.) create rules based on the given concepts or relations but they cannot create new concepts. However, in many cases one must use new intermediate concepts in order to form the recursive rules. We give a new technique for constructing such intermediate concepts and learning rules based on those concepts. We illustrate the new technique with several examples, none of which can be handled by the existing methods. We have implemented the new technique in Common Lisp and tested many different examples

Inductive logic programming using bounded hypothesis space

Inductive Logic Programming (ILP) systems apply inductive learning to an inductive learning task by deriving a hypothesis which explains the given examples. Applying ILP systems to real applications poses many challenges as they require large search space, noise is present in the learning task, and in domains such as software engineering hypotheses are required to satisfy domain specific syntactic constraints. ILP systems use language biases to define the hypothesis space, and learning can be seen as a search within the defined hypothesis space. Past systems apply search heuristics to traverse across a large hypothesis space. This is unsuitable for systems implemented using Answer Set Programming (ASP), for which scalability is a constraint as the hypothesis space will need to be grounded by the ASP solver prior to solving the learning task, making them unable to solve large learning tasks. This work explores how to learn using bounded hypothesis spaces and iterative refinement. Hypotheses that explain all examples are learnt by refining smaller partial hypotheses. This improves the scalability of ASP based systems as the learning task is split into multiple smaller manageable refinement tasks. The thesis presents how syntactic integrity constraints on the hypothesis space can be used to strengthen hypothesis selection criteria, removing hypotheses with undesirable structure. The notion of constraint-driven bias is introduced, where hypotheses are required to be acceptable with respect to the given meta-level integrity constraints. Building upon the ILP system ASPAL, the system RASPAL which learns through iterative hypothesis refinement is implemented. RASPAL's algorithm is proven, under certain assumptions, to be complete and consistent. Both systems have been applied to a case study in learning user's behaviours from data collected from their mobile usage. This demonstrates their capability for learning with noise, and the difference in their efficiency. Constraint-driven bias has been implemented for both systems, and applied to a task in specification revision, and in learning stratified programs.Open Acces

Inducción de conocimiento con incertidumbre en bases de datos relacionales borrosas

Este trabajo presenta un sistema para aprendizaje de definiciones lógicas con incertidumbre, a partir de una base de datos relacional borrosa. El campo de interés se centra, por tanto, en la programación lógica inductiva, introduciendo algunas interesantes aportaciones, principalmente en lo que se refiere a la entrada de datos y a los resultados producidos: Los datos de entrada pertenecen a una base de datos relacional borrosa. Por tanto, vienen expresados en forma de tablas de tuplas (relaciones), en las que las tuplas pueden llevar asociado un grado de pertenencia a la relación correspondiente. Se trata, por tanto, de relaciones borrosas, directamente identificables con conceptos borrosos (tan comunes en la realidad vista desde un punto de vista humano), y no de relaciones ordinarias con atributos borrosos (tal y como se entiende la "borrosidad" en muchos sistemas existentes). Los datos de salida vienen expresados en forma de definiciones lógicas de una relación (ordinaria o borrosa), que consta de una cláusula de Horn o de la disyunción de varias. Estas cláusulas de Horn se construyen mediante literales, aplicados sobre variables (generalmente), y asociados a relaciones borrosas u ordinarias. Los literales borrosos pueden ser modificados, además, por el empleo de etiquetas lingüísticas. Por tanto, se combina, en estas definiciones, la lógica de predicados con la lógica borrosa, en lo que podemos denominar "lógica borrosa de predicados", lo que constituye una aportación dentro de la inducción automática de conocimiento. Además, las definiciones inducidas llevan asociado un factor de incertidumbre, como hacen otros sistemas ya existentes. El punto de partida del trabajo lo constituye un sistema de inducción de definiciones lógicas bien conocido: FOIL, creado por Quinlan en 1990, basado en la lógica de predicados. Sobre este sistema inicial se realizan, además de las extensiones para lógica borrosa ya mencionadas, otra serie de modificaciones y ampliaciones enfocadas a mejorar la inducción de conocimiento. Estas mejoras se realizan, principalmente, en su parte heurística, al definir una función de evaluación de literales, basada en medidas de interés, que permite corregir algunas deficiencias del sistema original y aumentar la calidad de las reglas inducidas. Otras modificaciones se orientan hacia la introducción de conocimiento de base, mediante relaciones definidas intensionalmente, de modo similar a otros sistemas como FOCL. Como resultado tangible de la tesis, se ha desarrollado y probado un sistema, FZFOIL, disponible públicamente bajo la licencia GNU

Constraints on predicate invention

This chapter describes an inductive learning method that derives logic programs and invents predicates when needed. The basic idea is to form the least common anti-instance (LCA) of selected seed examples. If the LCA is too general it forms the starting poínt of a gneral-to-specific search which is guided by various constraints on argument dependencies and critical terms. A distinguishing feature of the method is its ability to introduce new predicates. Predicate invention involves three steps. First, the need for a new predicate is discovered and the arguments of the new predicate are determíned using the same constraints that guide the search. In the second step, instances of the new predicate are abductively inferred. These instances form the input for the last step where the definition of the new predicate is induced by recursively applying the method again. We also outline how such a system could be more tightly integrated with an abductive learning system