This chapter contains sections titled: 2.1 Introduction, 2.2 Agent Communications, 2.3 Agent Interaction Protocols, 2.4 Societies of Agents, 2.5 Conclusions, 2.6 Exercises, 2.7 References

Multiagent Systems and Societies of Agents

Fuzzy Prolog: a new approach using soft constraints propagation

We report an approach for implementing predictive fuzzy systems that manage capturing both the imprecision of the empirically induced classifications and the imprecision of the intuitive linguistic expressions via the extensive use of fuzzy sets. From end-users' point of view, the approach enables encapsulating the technical details of the underlying information system in terms of an intuitive linguistic interface. We describe a novel technical syntax of fuzzy descriptions and expressions, and outline the related systems of fuzzy linguistic queries and rules. To illustrate the method, we describe it in terms of a concrete educational user modelling application. We report experiments with two data sets, describing the records of the students attending to a university mathematics course in 2003 and 2004. In brief, we aim identifying the failing students of the year 2004, and develop a procedure for empirically inducing and assigning each student a fuzzy property "poor", which helps capturing the students needing extra assistance. In the educational context, the approach enables the construction of applications exploiting simple and intuitive student models, that to certain extent are self-evident.

Inducing Fuzzy Models for Student Classification.

RFuzzy: Syntax, semantics and implementation details of a simple and expressive fuzzy tool over Prolog

A poor theme definition for a degree project leads to problems in the project development in most of the cases. In the Faculty of Energy, Industries and Non-Renewable Natural Resources (FEIRNNR) approximately the 20% of the approved degree projects have failed causing unsureness in students and different points of view at the moment they have to choose their degree theme project. In the present work, how the definition of the theme for the degree project has influence in the future success or failure of the project was determined by using classification algorithms which are artificial intelligence techniques. The work started by cleaning the obtained dataset from the General Secretariat in the Faculty of Energy, Industries and Non-Renewable Natural Resources (FEIRNNR), then, the dataset was evaluated and the variables useful in the categorization of the projects were determined the one that had two categories: Success and Failure.
Openrefine was used as the tool for cleaning the information and RStudio, SPSS Statistics and RapidMiner were the tools used for the application of classification algorithms.
Dendogram, K-means, K-medoids, Apriori, Exhaustive CHAID and Random Forest were the algorithms and classification techniques applied to the dataset. The evaluation of these algorithms of classification was carried out by comparing the obtained results in each classification, so the results of the complete dataset which matched with success or failure cases were considered as the most relevant but the results of the success cases should not coincide with failure results due to they do not allow to determine the success or failure of the projects.
CHAID Exhaustive was the supervised algorithm of classification which provided a percentage of efficiency and relevant results in which it was found that the 32% of the approved projects to be develop in General Secretariat in the Faculty of Energy, Industries and Non-Renewable Natural Resources (FEIRNNR) failed and that if the words “management” and “system” are present in the theme of a degree work the project tends to culminate successfully, but if the works “mobile” and “application” are present in the theme, the degree work project tends to fail, so it was concluded that the definition of the theme of the degree work project have influence to the success or failure of it.

Aplicación de algoritmos de clasificación para determinar cómo influye la definición del tema de un proyecto de titulación en su éxito o fracaso

The result of introducing Fuzzy Logic into Logic Programming has been the development of several “Fuzzy Prolog” systems. These systems replace the inference mechanism of Prolog with a fuzzy variant which is able to handle partial truth as a real value or as an interval on [0,1]. Most of these systems consider only one operator to propagate the truth value through the fuzzy rules.

Fuzzy Prolog: A Simple General Implementation Using CLP($$\mathcal{R}$$)

The result of introducing Fuzzy Logic into Logic Programming has been the development of several Fuzzy Prolog systems. These systems replace the inference mechanism of Prolog with a fuzzy variant which is able to handle partial truth as a real value or as an interval on [0, 1]. Most of these systems consider only one operator to propagate the truth value through the fuzzy rules. We aim at defining a Fuzzy Prolog Language in a general way and to provide an implementation of a Fuzzy Prolog System for our general approach that is extraordinary simple thanks to the use of constraints. Our approach is general in two aspects: (i) Truth value will be a countable union of sub-intervals on [0,1], representation also called Borel Algebra over this interval, B([0, 1]). Former representations of truth value are particular cases of this definition and many real fuzzy problems only can be modeled using this representation. (ii) The concept of aggregation generalizes the computable operators. It subsumes conjunctive operators (triangular norms as min, prod, etc), disjunctive operators (triangular co-norms as max, sum, etc), average operators (arithmetic average, cuasi-linear average, etc) and hybrid operators (combinations of previous operators). We define and use aggregation operator for our language instead of limiting ourselves to a particular one. Therefore, we have implemented several aggregation operators and others can be added to the system with little effort. We have incorporated uncertainty into a Prolog system in a simple way. This extension to Prolog is realized by interpreting fuzzy reasoning (truth values and the result of aggregations) as a set of constraints then translating fuzzy rules into CLP(R) clauses. The implementation is based on a syntactic expansion of the source code at compilation-time. The novelty of the Fuzzy Prolog presented is that it is implemented over Prolog, using its resolution system, instead of implementing a new resolution system such as other approaches. The current implementation is a syntactic extension that uses the CLP(R) system of Ciao Prolog. Lastest distributions includes our Fuzzy Prolog implementation and can be downloaded from http://www.clip.dia.fi.upm.es/Software. Our approach can be easily implemented on other CLP(R) system.

Fuzzy prolog: A simple general implementation using CLP(R)

This paper is a brief continuation of earlier work by the same authors [4] and [5] that deals with the concepts of conjecture, hypothesis and consequence in orthocomplemented complete lattices. It considers only the following three points: 1. Classical logic theorems of both deduction and contradiction are reinterpreted and proved by means of one specific operator C∧ defined in [4]. 2. Having shown that there is reason to consider the set C∧(P) of consequences of a set of premises P as too large, it is proven that C∧(P) is the largest set of consequences that can be assigned to P by means of a Tarski's consequences operator, provided that L is a Boolean algebra. 3. On the other hand, it is proven that, also in a Boolean algebra, the set Φ∧(P) of strict conjectures is the smallest of any Φ(P) such that P ⊆ Φ(P) and th at if P ⊆ Q then Φ(Q) ⊆ Φ(P).

Additional Comments on Conjectures, Hypotheses, and Consequences in Orthocomplemented Lattices

Resumen En este trabajo se presenta un framework para la implementación de agentes deliberativos cuyo razonamiento está basado en planificación continua. El planificador continuo es definido como una especialización del planificador de orden parcial con el agregado de caracteŕısticas que lo hacen apto para ambientes dinámicos. La arquitectura del framework contempla también la ejecución concurrente de los procesos del planificador y el controlador del agente, aśı como la sincronización de los mismos. Asimismo, se muestra el uso del framework en el dominio del fútbol con robots, y se resaltan importantes aspectos de la implementación.

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Agentes deliberativos basados en planificación continua

/pdf/combining-crisp-and-fuzzy-logic-in-a-prolog-compiler-4efqmwge7t.pdf

Claudio A. Vaucheret

Papers

Fuzzy Prolog: A Simple General Implementation Using CLP($$\mathcal{R}$$)

Fuzzy prolog: A simple general implementation using CLP(R)

Additional Comments on Conjectures, Hypotheses, and Consequences in Orthocomplemented Lattices

Agentes deliberativos basados en planificación continua

Combining Crisp and Fuzzy Logic in a Prolog Compiler.