We propose a new family of quantum computing algorithms which generalize the Deutsch-Jozsa, Simon and Shor ones. The goal of our algorithms is to estimate conditional probability distributions. Such estimates are useful in applications of Decision Theory and Artificial Intelligence, where inferences are made based on uncertain knowledge. The family of algorithms that we propose is based on a co...

The Semantic Web [1] extends the World Wide Web by providing well-defined semantics to information and services. Through these semantics machines can “understand” the Web, making it possible to query and reason over Web information, treating the Web as if it were a giant semi-structured database. Over the recent years, large volumes of data have been published in a Semantic Web format, constitu...

Expert systems applications that involve uncertain inference can be represented by a multidimensional contingency table. These tables offer a general approach to inferring with uncertain evidence, because they can embody any form of association between any number of pieces of evidence and conclusions. (Simpler models may be required, however, if the number of pieces of evidence bearing on a con...

The process of designing artifacts is a creative activity. It is proposed that, at the cognitive level, one key to understanding design creativity is to understand the array of symbol systems designers utilize. These symbol systems range from being vague, imprecise, abstract, ambiguous, and indeterminate (like conceptual sketches), to being very precise, concrete, unambiguous, and determinate (...

The pervasive use of AI today caused an urgent need for human-compliant approaches and solutions that can explain their behavior decisions in human-understandable terms, especially critical domains, so as to enforce trustworthiness support accountability. symbolic/logic approach supports this because it aims at reproducing human reasoning mechanisms. While much research has been carried out on ...

In complex and frequently changing product domains, customers need qualified consultation services that allow them to make well-informed purchasing decisions without requiring excessive technical knowledge about the product domain. In this paper we present an intuitive customer metamodel with an associated inference engine which can be integrated into conversational recommender systems to provi...

The Engine for Likelihood-Free Inference (ELFI) is a Python software library for performing likelihood-free inference (LFI). ELFI provides a convenient syntax for specifying LFI models commonly composed of priors, simulators, summaries, distances and other custom operations. These can be implemented in a wide variety of languages. Separating the modelling task from the inference makes it possib...

Lines In[1] and Out[2] below demonstrate a simple interaction with through a notebook interface.1 On line In[1] , the type function object is applied on a representation of the MATLAB expression tanh(3.78i). ’s response, shown on Out[2] , is the inferred type of tanh(3.78i). In this case, “type” is the expression {v, i, s} where v, i and s are the value range, intrinsic type and array shape of ...