Real-world tasks often involve a continuous flow of new information that affects the learned theo... more Real-world tasks often involve a continuous flow of new information that affects the learned theory, a situation that classical batch (one-step) learning systems are hardly suitable to handle. On the contrary, incremental (also called "on-line") techniques are able to deal ...
This work concerns a research project aiming at studying whether a machine learning system could ... more This work concerns a research project aiming at studying whether a machine learning system could reproduce the changes in the concept of force observed in children. The theoretical framework proposed considers learning as a process of formation and revision of a logical theory. INTHELEX, an incremental learning system, was used to emulate the transitions occurring in the human learning process.
Markov Logic (ML) combines Markov networks (MNs) and first-order logic by attaching weights to fi... more Markov Logic (ML) combines Markov networks (MNs) and first-order logic by attaching weights to first-order formulas and using these as templates for features of MNs. However, MAP and conditional inference in ML are hard computational tasks. This paper presents ...
ABSTRACT The spread and abundance of electronic documents requires automatic techniques for extra... more ABSTRACT The spread and abundance of electronic documents requires automatic techniques for extracting useful information from the text they contain. The availability of conceptual taxonomies can be of great help, but manually building them is a complex and costly task. Building on previous work, we propose a technique to automatically extract concep- tual graphs from text and reason with them. Since automated learning of taxonomies needs to be robust with respect to missing or partial knowl- edge and flexible with respect to noise, this work proposes a way to deal with these problems. The case of poor data/sparse concepts is tackled by finding generalizations among disjoint pieces of knowledge. Noise is handled by introducing soft relationships among concepts rather than hard ones, and applying a probabilistic inferential setting. In particu- lar, we propose to reason on the extracted graph using different kinds of relationships among concepts, where each arc/relationship is associated to a weight that represents its likelihood among all possible worlds, and to face the problem of sparse knowledge by using generalizations among distant concepts as bridges between disjoint portions of knowledge.
ABSTRACT Horn clause Logic is a powerful representation language exploited in Logic Programming a... more ABSTRACT Horn clause Logic is a powerful representation language exploited in Logic Programming as a computer programming framework and in Inductive Logic Programming as a formalism for expressing examples and learned theories in domains where relations among objects must be expressed to fully capture the relevant information. While the predicates that make up the description language are defined by the knowledge engineer and handled only syntactically by the interpreters, they sometimes express information that can be properly exploited only with reference to a suitable background knowledge in order to capture unexpressed and underlying relationships among the concepts described. This is typical when the representation includes numerical information, such as single values or intervals, for which simple syntactic matching is not sufficient. This work proposes an extension of an existing framework for similarity assessment between First-Order Logic Horn clauses, that is able to handle numeric information in the descriptions. The viability of the solution is demonstrated on sample problems.
This chapter proposes an approach for the cooperation of abduction and induction in the context o... more This chapter proposes an approach for the cooperation of abduction and induction in the context of Logic Programming. We do not take a stance on the debate on the nature of abduction and induction (see Flach and Kakas, this volume), rather we assume the definitions that are ...
Abstract. This paper presents a logic framework for the incremental inductive synthesis of Datalo... more Abstract. This paper presents a logic framework for the incremental inductive synthesis of Datalog theories. It allows us to cast the problem as a process of abstract diagnosis and debugging of an incorrect theory. This process involves a search in a space, whose algebraic structure ( ...
Real-world tasks often involve a continuous flow of new information that affects the learned theo... more Real-world tasks often involve a continuous flow of new information that affects the learned theory, a situation that classical batch (one-step) learning systems are hardly suitable to handle. On the contrary, incremental (also called "on-line") techniques are able to deal ...
This work concerns a research project aiming at studying whether a machine learning system could ... more This work concerns a research project aiming at studying whether a machine learning system could reproduce the changes in the concept of force observed in children. The theoretical framework proposed considers learning as a process of formation and revision of a logical theory. INTHELEX, an incremental learning system, was used to emulate the transitions occurring in the human learning process.
Markov Logic (ML) combines Markov networks (MNs) and first-order logic by attaching weights to fi... more Markov Logic (ML) combines Markov networks (MNs) and first-order logic by attaching weights to first-order formulas and using these as templates for features of MNs. However, MAP and conditional inference in ML are hard computational tasks. This paper presents ...
ABSTRACT The spread and abundance of electronic documents requires automatic techniques for extra... more ABSTRACT The spread and abundance of electronic documents requires automatic techniques for extracting useful information from the text they contain. The availability of conceptual taxonomies can be of great help, but manually building them is a complex and costly task. Building on previous work, we propose a technique to automatically extract concep- tual graphs from text and reason with them. Since automated learning of taxonomies needs to be robust with respect to missing or partial knowl- edge and flexible with respect to noise, this work proposes a way to deal with these problems. The case of poor data/sparse concepts is tackled by finding generalizations among disjoint pieces of knowledge. Noise is handled by introducing soft relationships among concepts rather than hard ones, and applying a probabilistic inferential setting. In particu- lar, we propose to reason on the extracted graph using different kinds of relationships among concepts, where each arc/relationship is associated to a weight that represents its likelihood among all possible worlds, and to face the problem of sparse knowledge by using generalizations among distant concepts as bridges between disjoint portions of knowledge.
ABSTRACT Horn clause Logic is a powerful representation language exploited in Logic Programming a... more ABSTRACT Horn clause Logic is a powerful representation language exploited in Logic Programming as a computer programming framework and in Inductive Logic Programming as a formalism for expressing examples and learned theories in domains where relations among objects must be expressed to fully capture the relevant information. While the predicates that make up the description language are defined by the knowledge engineer and handled only syntactically by the interpreters, they sometimes express information that can be properly exploited only with reference to a suitable background knowledge in order to capture unexpressed and underlying relationships among the concepts described. This is typical when the representation includes numerical information, such as single values or intervals, for which simple syntactic matching is not sufficient. This work proposes an extension of an existing framework for similarity assessment between First-Order Logic Horn clauses, that is able to handle numeric information in the descriptions. The viability of the solution is demonstrated on sample problems.
This chapter proposes an approach for the cooperation of abduction and induction in the context o... more This chapter proposes an approach for the cooperation of abduction and induction in the context of Logic Programming. We do not take a stance on the debate on the nature of abduction and induction (see Flach and Kakas, this volume), rather we assume the definitions that are ...
Abstract. This paper presents a logic framework for the incremental inductive synthesis of Datalo... more Abstract. This paper presents a logic framework for the incremental inductive synthesis of Datalog theories. It allows us to cast the problem as a process of abstract diagnosis and debugging of an incorrect theory. This process involves a search in a space, whose algebraic structure ( ...
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