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

Prolegomenon to a media theory of machine learning

This paper, outlines a groundwork for a media theory of machine learning by introducing two new concepts, compute-computing and compute-computed, and a framework for their interaction. Compute-computing (computing as generative) is here understood as the “active” learning component of a system, whereas compute-computed (computing as generated) is understood as the “passive”, coded, imprinted or inscribed aspect of a system. I introduce these two concepts to help us to think through the specificity of algorithmic systems that are more than just the operative, sequential or parallel systems of computational processing to which we have become accustomed. Indeed, in the case of machine learning systems, these systems have the capacity to be self-positing in the sense of generating models and data structures that internalise certain pattern characteristics of data, without the requirement that they are translated into formal data structures by a human programmer. That is, they are able to capture the abstract form of data input into the system, identify key characteristics, frames or patterns, and store this for comparison and classification of other data streams or objects

Biobutanol and bioethanol production from agricultural wastes: A cell phone application for computing the bioconversion rates

To carry out the calculations required for modelling and computing for kinetics biobutanol and bioethanol yields and production rates, several procedures should be accomplished; this requires time and effort, and there is a chance that mistakes will be made. The goal of this study is to provide a tool that will assist users, engineers, and experts in conducting these computations by creating a mobile application to reduce time and effort. The calculations were carried out using a mathematical model. The mathematical model was then included in a flowchart that was created later. After that, Kodular was used to configure the mobile application by fusing the interface design, mathematical model, and flowchart. Information was gathered from publications, wastewater treatment facilities, non-governmental organizations (NGOs), and government groups. To offer output data that matched the output data obtained from the configured program, the data collected for doing the calculations in the conventional manner was used. Both the standard strategy and the program's outcomes were consistent. The created mobile application can do kinetic modeling and determine the yields and rates of generation of biobutanol and bioethanol from agricultural waste

Simulation of Gene Regulatory Networks

This limited review is intended as an introduction to the fast growing subject of mathematical modelling of cell metabolism and its biochemical pathways, and more precisely on pathways linked to apoptosis of cancerous cells. Some basic mathematical models of chemical kinetics, with emphasis on stochastic models, are presented

Approximate and Situated Causality in Deep Learning

Altres ajuts: ICREA Academia 2019, and "AppPhil: Applied Philosophy for the Value-Design of Social Networks Apps" project, funded by Caixabank in Recercaixa2017.Causality is the most important topic in the history of western science, and since the beginning of the statistical paradigm, its meaning has been reconceptualized many times. Causality entered into the realm of multi-causal and statistical scenarios some centuries ago. Despite widespread critics, today deep learning and machine learning advances are not weakening causality but are creating a new way of finding correlations between indirect factors. This process makes it possible for us to talk about approximate causality, as well as about a situated causality

Explainable and Ethical AI: A Perspective on Argumentation and Logic Programming

In this paper we sketch a vision of explainability of intelligent systems as a logic approach suitable to be injected into and exploited by the system actors once integrated with sub-symbolic techniques. In particular, we show how argumentation could be combined with different extensions of logic programming – namely, abduction, inductive logic programming, and probabilistic logic programming – to address the issues of explainable AI as well as some ethical concerns about AI