Computer programming is experiencing a paradigm shift driven by the advent of powerful large language model–based tools for automatic source code generation. This transformation is also reshaping introductory programming courses worldwide, raising critical questions on how programming should be taught, learned, and assessed in the era of generative artificial intelligence.This article pursues two objectives. First, it reviews the main studies addressing this issue, emphasizing the reported advantages and limitations. Second, it proposes enhancements to teaching and learning methodologies by prioritizing code comprehension and execution over code writing or functionality. Specifically, the use of visual code representations and execution simulations is proposed as an effective approach for teaching, learning, and assessing programming, ensuring a deeper conceptual understanding among students.Finally, the article reports preliminary insights from students enrolled in an object-oriented programming course, providing contextual evidence to support the integration of visual simulations in Java (and other programming languages) within programming education.

This work presents the definition of a water risk index based on temperatures and precipitation during the years 1979 to 2019 in the Ñuble Region (Chile). Its calculation has been implemented in a software application that allows working with multidimensional data files in NetCDF format. In addition, our prototype allows to load and visualize this type of data in an easy way without the need of having an advanced knowledge in the management of geographic information systems. The result is an application that we would like to use in the future to improve water management and decision making in the region.

Bousi Prolog is a first-order fuzzy logic programming language whose operational semantics is an adaptation of the SLD resolution principle and whose fuzzy unification algorithm is based on proximity relations. This programming language is suitable for dealing with query answering processes, advanced pattern matching, flexible deductive databases, knowledge-based systems and approximate reasoning. Specifically, Bousi Prolog has already been used in interesting real applications such as text cataloguing, knowledge discovery and linguistic feedback in computer games.This paper presents an initial study on the incorporation of the weak unification with fuzzy functor/arity mismatch, recently introduced by Aït-Kaci and Pasi, in the Bousi Prolog system. This fuzzy unification algorithm will allow to Bousi Prolog to obtain answers for query processes in which first-order terms with different arity and terms ordering are considered. Any system using unification mechanisms on imperfect domains, whose data can be inaccurate or uncertain, can be benefited from the approach presented in this paper.

Recently, we have presented a new technology for improving player experience in Computer Games by using players behaviour analysis and linguistic descriptions. Here, we explain the details about its implementation which have not been presented in any work before. Our implementation is based on a declarative version of the concept of computational perception network whose implementation has been performed by means of a fuzzy linguistic logic programming language named Bousi Prolog. Our aim is to show the potentiality of this kind of programming language in order to implement computational perceptions in computer games.

This paper aims to propose a model of reasoning based on semantic relations among words and to incorporate it in the inference mechanism of a logic programming language. This model is integrated in a fuzzy logic programming framework and it is implemented into the Bousi~Prolog system by using WordNet. All this process is transparent to the programmer and the reasoning with words is automatic. The lexical semantics between symbols (words) provides us with the ability of reasoning with words and turns the knowledge representation in a more natural and less complex process.

Bousi~Prolog is a fuzzy logic programming language whose main objective is to make flexible the query answering process. Its operational mechanism is a extension of the SLD-resolution (called weak resolution) where the classical syntactic unification algorithm has been replaced by a fuzzy one. This paper presents a generic method for the unification of linguistic terms (i.e. fuzzy sets) which is also applicable to other programming languages with an operational semantics based on some kind of weak resolution mechanism. The basic idea is to compile the information provided by fuzzy sets, generating a binary fuzzy relation on the set of their associated linguistic labels. Subsequently, this fuzzy relation can be used in a standard, completely integrated way inside the unification mechanism of the Bousi~Prolog system, what allows us to handle linguistic labels on an equal basis with regard other syntactic symbols occurring in the source program. This is a novel approach because it is the first time that fuzzy sets are introduced into the core of a Prolog system by means of compilation techniques and combining fuzzy relations with weak unification. An important feature of this approach is its simplicity, since the inclusion is carried out in a very natural way without affecting the operational semantics of the Bousi~Prolog language and with very few syntactical modifications. All these reasons convert our approach in a good alternative to the techniques used by other fuzzy Prolog systems.

Bousi~Prolog is a fuzzy logic programming language with an operational semantics which is an adaptation of the SLD resolution principle, where classical unification has been replaced by a fuzzy unification algorithm based on proximity relations. Hence, it is a programming language well suited for dealing with uncertainty and approximate reasoning. There are several practical applications where Bousi~Prolog can be useful: flexible query answering; advanced pattern matching; information retrieval where textual information is selected or analyzed using an ontology; text cataloging and analysis; etc. In this paper we give a model-theoretic semantics for a pure subset of this language: we formalize the notion of least fuzzy Herbrand model as the declarative semantics for definite programs. We prove various important properties of these models. Finally we define an immediate consequences operator, which is proved monotonous and continuous, obtaining a fixpoint characterization of the least fuzzy Herbrand model.