Motivation Perhaps the greatest challenge of modern biology is to develop accurate in silico models of cells. To do this we require computational formalisms for both simulation (how according to the model the state of the cell evolves over time), and identification (learning a model cell from observation of states). We propose the use of qualitative reasoning (QR) as a unified formalism for bot...

A theory learned by an inductive logic programming (ILP) system such as Progol [5] usually comprises a set of concepts, expressed as logic programs, which can be employed for a classification task. This classifying ability can, in turn, be used for prediction tasks. A scientific theory, however, comprises much more information: concepts; hypotheses relating concepts; explanations and empirical ...

Learning programs with numerical values is fundamental to many AI applications, including bio-informatics and drug design. However, current program synthesis approaches struggle learn values. An especially difficult problem learning continuous from multiple examples, such as intervals. To overcome this limitation, we introduce an inductive logic programming approach which combines relational re...

there are fewer problems encountered with optical voltage transducers in comparison with their inductive and capacitive counterparts. although capacitive and inductive transformers are used vastly for the purpose of measurement and protection in the networks, they cause problems such as core saturation and improper transient responses which decrease the accuracy of measurement and eficiency of ...

Inductive probability is the logical concept of probability in ordinary language. It is vague but it can be explicated by defining a clear and precise concept that can serve some of the same purposes. This paper presents a general method for doing such an explication and then a particular explication due to Carnap. Common criticisms of Carnap’s inductive logic are examined; it is shown that mos...

This essay defends the view that inductive reasoning involves following inductive rules against objections that inductive rules are undesirable because they ignore background knowledge and unnecessary because Bayesianism is not an inductive rule. I propose that inductive rules be understood as sets of functions from data to hypotheses that are intended as solutions to inductive problems. Accord...

We discuss our on-going research on making inductive types cumulative in the predicative calculus of inductive constructions (pCIC) – the logic of the Coq proof assistant. Having inductive types be cumulative alleviates some problems that occur while working with large inductive types, e.g., the category of small categories, in pCIC. We present the pCuIC system which adds cumulativity for induc...

Automated reasoning of inductive theorems is considered important in program verification. To verify inductive theorems automatically, several implicit induction methods like the inductionless induction and the rewriting induction methods have been proposed. In studying inductive theorems on higher-order rewritings, we found that the class of the theorems shown by known implicit induction metho...

This paper reviews our recent work on applying inductive logic programming to the construction of natural language processing systems. We have developed a system, Chill, that learns a parser from a training corpus of parsed sentences by inducing heuristics that control an initial overly-general shift-reduce parser. Chill learns syntactic parsers as well as ones that translate English database q...