Argument based machine learning

AbstractWe present a novel approach to machine learning, called ABML (argumentation based ML). This approach combines machine learning from examples with concepts from the field of argumentation. The idea is to provide expert's arguments, or reasons, for some of the learning examples. We require that the theory induced from the examples explains the examples in terms of the given reasons. Thus arguments constrain the combinatorial search among possible hypotheses, and also direct the search towards hypotheses that are more comprehensible in the light of expert's background knowledge. In this paper we realize the idea of ABML as rule learning. We implement ABCN2, an argument-based extension of the CN2 rule learning algorithm, conduct experiments and analyze its performance in comparison with the original CN2 algorithm

Postulates for logic-based argumentation systems

International audienceLogic-based argumentation systems are developed for reasoning with inconsistent information. Starting from a knowledge base encoded in a logical language, they define arguments and attacks between them using the consequence operator associated with the language. Finally, a semantics is used for evaluating the arguments. In this paper, we focus on systems that are based on deductive logics and that use Dung's semantics. We investigate rationality postulates that such systems should satisfy. We define five intuitive postulates: consistency and closure under the consequence operator of the underlying logic of the set of conclusions of arguments of each extension, closure under sub-arguments and exhaustiveness of the extensions, and a free precedence postulate ensuring that the free formulas of the knowledge base (i.e., the ones that are not involved in inconsistency) are conclusions of arguments in every extension. We study the links between the postulates and explore conditions under which they are guaranteed or violated

Ugotavljanje preferenc z uporabo argumentiranega strojnega učenja

We have developed a novel method for determining people\u27s preferences based on their explanations of visual data. To this end, we have extended the existing framework for argument-based machine learning (ABML), which includes argument-based rule learning and an interactive knowledge refinement loop, with a recommendation engine and a pipeline based on convolutional neural networks to obtain interpretable data from images. We have developed an interactive application inspired by ABML to determine users\u27 dating preferences. To enable a user to argue and explain his preferences based on image data, we introduced a novel approach where the user explains his preferences by drawing rectangles to select a part of the image he likes or dislikes. The ABML knowledge refinement loop allows the user to focus on the most critical parts of the current knowledge base and helps the user to adequately explain selected relevant examples - in our case, images. We have shown experimentally that the new approach to preference elicitation allows successful preference elicitation when it comes to dating. All users found the final selection of images useful, and the selection of images that the user is likely to prefer gradually improved during the interaction. The identified preferences of each user of the application are presented as a rule-based model that helps to quickly find images according to the user\u27s taste. We have shown that this rule model is easy to interpret. All participants found that most of the rules in the final model matched their preferences. The beauty of our approach to preference elicitation is that, at least in principle, we can address any domain that can be represented by images, where people can explain which parts of the image they like or dislike, provided that it is possible to obtain meaningful attributes from images.V magistrskem delu smo razvili novo metodo za določanje uporabnikovih preferenc, ki temelji na analizi slik in uporabnikovih argumentov o slikah. V ta namen smo razširili obstoječe ogrodje za argumentirano strojno učenje (ABML), ki vključuje argumentirano učenje pravil in interaktivno zanko za zajemanje znanja. Zanka vključuje priporočilni sistem in cevovod, ki temelji na konvolucijskih nevronskih mrežah, s katerimi iz slik dobimo atribute, ki jih je mogoče interpretirati. Za namen določitve preferenc uporabnikov smo razvili interaktivni vmesnik, ki sloni na metodi ABML. Uporabniku smo omogočili, da lahko svoje preference utemelji in pojasni glede na atribute na sliki. Za ta namen smo uporabili nov pristop, pri katerem lahko uporabnik svoje preference razloži z označevanjem delov slike. S pravokotnikom lahko izbere del slike, ki mu je všeč ali ne. Zanka za zajemanje znanja ABML uporabniku omogoča, da se osredotoči na najbolj kritične dele trenutne baze znanja in mu pomaga, da ustrezno razloži izbrane slike. S tremi udeleženci smo izvedli poizkuse v domeni slik ljudi in tako pokazali, da nov pristop omogoča uspešno pridobivanje uporabnikovih preferenc. Vsem udeležencem se je zdel končni izbor slik uporaben. Število všečkov slik, ki jih je predlagala metoda, se je postopoma izboljševalo. Končne preference vsakega uporabnika aplikacije so predstavljene kot model, ki temelji na pravilih in pomaga hitro najti slike, ki ustrezajo uporabniku. Pokazali smo, da je ta model pravil enostavno interpretirati. Vsi udeleženci so ugotovili, da se večina pravil v končnem modelu ujema z njihovimi preferencami. Prednosti obravnavanega pristopa pri ugotavljanju preferenc je v širini spektra, ki ga pokriva. Načeloma je z njim mogoče nasloviti katero koli domeno s slikami. Da je uporaba tega pristopa smiselna, morajo biti uporabniki zmožni pojasniti, kateri deli slike so jim všeč ali ne, hkrati pa mora biti uresničen pogoj, da je iz slik mogoče pridobiti smiselne atribute