Halpern 1990; Smets 1991; Yu and Arasta 1994), Dempster's conditional and Fagin-Halpern (FH) conditional can be considered the most widely used two DST conditional The flexibility and expressiveness of Dempster-Shafer (DS) A widely used approach for carrying out precise computation theoretic models make DS evidence theory (Dempster 1967; of the Dempster's conditional is a matrix calculus 1968; Shafer 1976) an ideal framework for reasoning and based algorithm which generates the Dempster's conditional decision making under uncertainty in Artificial Intelligence masses (Klawonn and Smets 1992; Smets 2002). Therefore, this specialization matrix-based method imposes Computing the DST belief functions and the DST conditionals, a prohibitive burden when dealing with larger FoDs.

Forgetting as a knowledge management operation has received much less attention than operations like inference, or revision. It was mainly in the area of logic programming that techniques and axiomatic properties have been studied systematically. However, at least from a cognitive view, forgetting plays an important role in restructuring and reorganizing a human's mind, and it is closely related to notions like relevance and independence which are crucial to knowledge representation and reasoning. In this paper, we propose axiomatic properties of (intentional) forgetting for general epistemic frameworks which are inspired by those for logic programming, and we evaluate various forgetting operations which have been proposed recently by Beierle et al. according to them. The general aim of this paper is to advance formal studies of (intentional) forgetting operators while capturing the many facets of forgetting in a unifying framework in which different forgetting operators can be contrasted and distinguished by means of formal properties.

The accuracy of Top-N recommendation task is challenged in the systems with mainly implicit user feedback considered. Adversarial training has presented successful results in identifying real data distributions in various domains (e.g. image processing). Nonetheless, adversarial training applied to recommendation is still challenged especially by interpretation of negative implicit feedback causing it to converge slowly as well as affecting its convergence stability. This is often attributed to high sparsity of the implicit feedback and discrete values characteristic from items recommendation. To face these challenges, we propose a novel model named convolutional adversarial latent factor model (CALF), which uses adversarial training in generative and discriminative models for implicit feedback recommendations. We assume that users prefer observed items over generated items and then apply pairwise product to model the user-item interactions. Additionally, the latent features become input data of our convolutional neural network (CNN) to learn correlations among embedding dimensions. Finally, Rao-Blackwellized sampling is adopted to deal with the discrete values optimizing CALF and stabilizing the training step. We conducted extensive experiments on three different benchmark datasets, where our proposed model demonstrates its efficiency for item recommendation.

Many recommender systems suffer from popularity bias: popular items are recommended frequently while less popular, niche products, are recommended rarely or not at all. However, recommending the ignored products in the ``long tail'' is critical for businesses as they are less likely to be discovered. In this paper, we introduce a personalized diversification re-ranking approach to increase the representation of less popular items in recommendations while maintaining acceptable recommendation accuracy. Our approach is a post-processing step that can be applied to the output of any recommender system. We show that our approach is capable of managing popularity bias more effectively, compared with an existing method based on regularization. We also examine both new and existing metrics to measure the coverage of long-tail items in the recommendation.

Auditing algorithms has emerged as a methodology for holding algorithms accountable by testing whether they are fair. This process often relies on the repeated use of a platform to record inputs and their corresponding outputs. For example, to audit Google search, one repeatedly inputs queries and captures the received search pages. The goal is then to discover, in the collected data, patterns that will reveal the ``secrets'' of algorithmic decision making. This knowledge discovery process makes some algorithm auditing tasks great applications for data mining techniques. In this paper, we introduce one particular algorithm audit, that of Google's Top stories. We describe the process of data collection, exploration, and analysis for this application and share some of the gleaned insights. Concretely, our analysis suggests that Google might be trying to burst the famous ``filter bubble'' by choosing less known publishers for the 3rd position in the Top stories.

This paper is about the comparison of content-dependent and content-independent features for the identification of short texts author’s age range and gender. Eight content-dependent features based on profiles of ngrams of words are used. In addition, ninety-eight content-independent features covering all the linguistic aspects of texts from phonology to discourse are used. These features were extracted from three corpora of different sizes and types. Were also conducted some experiments using four different machine learning algorithms combined with these features. The results show that content-dependent features do a better job for gender identification on the three corpora. However, content-independent features did better with the task of age range identification.

Traditional case-based classification methods are based on feature similarity. In contrast, class-to-class (C2C) weighting also considers whether the difference between two cases has been seen before. Combined with instance-specific weighting, C2C weighting learns the local patterns of both similarities and differences (shortened as patterns). Once C2C weightings has learned the pattern between case A of class C_1 and some set of cases R of class C_2, given a query Q whose difference from A matches the pattern between A and R, then we can skip cases around A and continue the search for near neighbors around R. Based on this, we developed an algorithm, C2C trace retrieval, which quickly traverses promising cases, retrieves relevant cases from different classes, and provides an informed hypothesis of the query's class. C2C trace retrieval achieves great efficiency at a reasonable cost of accuracy. Therefore, C2C trace retrieval can be used as a fast classification method or as the first pass for a more sophisticated method.

In the field of argumentation, the vision of robust argumentation machines is investigated. They explore natural language arguments from available information sources on the web and reason with them on the knowledge level to actively support the deliberation and synthesis of arguments for a particular query of a user. We aim at combining methods from case-based reasoning (CBR), information retrieval, and computational argumentation to contribute to the foundations of such argumentation machines. In this paper, we focus on the retrieval phase of a CBR approach for an argumentation machine and propose similarity measures for arguments represented as argument graphs. We evaluate the similarity measures on a corpus of annotated micro texts containing different topics and demonstrate the benefit of semantic similarity measures as well as the relevance of structural aspects.

Information collection is an important application of multi-robot systems especially in environments that are difficult to operate for humans. The objective of the robots is to maximize information collection from the environment while remaining in their path-length budgets. In this paper, we propose a novel multi-robot information collection algorithm that uses a continuous region partitioning approach to efficiently divide an unknown environment among the robots based on the discovered obstacles in the area, for better load-balancing. Our algorithm gracefully handles situations when some of the robots cannot communicate with other robots due to limited communication ranges.

Modelling biologically-plausible neural structures for intelligent agents presents a unique challenge when operating in real-time domains. Neurons in our brains have different response properties, firing rates, and propagation lengths, creating noise that cannot be reliably decoded. This research explores the strengths and limitations of LIF spiking neuron ensembles for application in OpenAI virtual environments. Topics discussed include how we represent arbitrary environmental signals from multiple senses, choosing between equally viable actions in a given scenario, and how one can create a generic model that can learn and operate in a verity of situations.