Raven’s Progressive Matrices (RPMs) are a popular family of general intelligence tests, and provide a non-verbal measure of a test subject’s reasoning abilities. Traditionally RPMs have been manually designed. To make them readily available for both practice and examination, we tackle the problem of automatically synthesizing RPMs. Our goal is to efficiently generate a large number of RPMs that are authentic (i.e. similar to manually written problems), interesting (i.e. diverse in terms of difficulty), and well-formed (i.e unambiguous). The main technical challenges are: How to formalize RPMs to accommodate their seemingly enormous diversity, and how to define and enforce their validity? To this end, we (1) introduce an abstract representation of RPMs using first-order logic, and (2) restrict instantiations to only valid RPMs. We have realized our approach and evaluated its efficiency and effectiveness. We show that our system can generate hundreds of valid problems per second with varying levels of difficulty. More importantly, we show, via a user study with 24 participants, that the generated problems are statistically indistinguishable from actual problems. This work is an exciting instance of how logic and reasoning may aid general learning.

In this article we present DUAL-PECCS, an integrated Knowledge Representation system aimed at extending artificial capabilities in tasks such as conceptual categorization. It relies on two different sorts of cognitively inspired common-sense reasoning: prototypical reasoning and exemplars-based reasoning. Furthermore, it is grounded on the theoretical tenets coming from the dual process theory of the mind, and on the hypothesis of heterogeneous proxytypes, developed in the area of the biologically inspired cognitive architectures (BICA). The system has been integrated into the ACT-R cognitive architecture, and experimentally assessed in a conceptual categorization task, where a target concept illustrated by a simple common-sense linguistic description had to be identified by resorting to a mix of categorization strategies. Compared to human-level categorization, the obtained results suggest that our proposal can be helpful in extending the representational and reasoning conceptual capabilities of standard cognitive artificial systems.

Hierarchies of concepts are useful in many applications from navigation to organization of objects. Usually, a hierarchy is created in a centralized manner by employing a group of domain experts, a time-consuming and expensive process. The experts often design one single hierarchy to best explain the semantic relationships among the concepts, and ignore the natural uncertainty that may exist in the process. In this paper, we propose a crowdsourcing system to build a hierarchy and furthermore capture the underlying uncertainty. Our system maintains a distribution over possible hierarchies and actively selects questions to ask using an information gain criterion. We evaluate our methodology on simulated data and on a set of real world application domains. Experimental results show that our system is robust to noise, efficient in picking questions, cost-effective, and builds high quality hierarchies.

The popularization and quick growth of Linked Open Data (LOD) has led to challenging aspects regarding quality assessment and data exploration of the RDF triples that shape the LOD cloud.Particularly, we are interested in the completeness of data and its potential to provide concept definitions in terms of necessary and sufficient conditions.In this work we propose a novel technique based on Formal Concept Analysis which organizes RDF data into a concept lattice.This allows data exploration as well as the discovery of implications, which are used to automatically detect missing information and then to complete RDF data.Moreover, this is a way of reconciling syntax and semantics in the LOD cloud.Finally, experiments on the DBpedia knowledge base show that the approach is well-founded and effective.

An author’s profile on Google Scholar consists of indexed articles and associated data, such as the number of citations and the H-index. The author is allowed to merge articles, which may affect the H-index. We analyze the parameterized complexity of maximizing the H-index using article merges. Herein, to model realistic manipulation scenarios, we define a compatability graph whose edges correspond to plausible merges. Moreover, we consider multiple possible measures for computing the citation count of a merged article. For the measure used by Google Scholar, we give an algorithm that maximizes the H-index in linear time if the compatibility graph has constant-size connected components. In contrast, if we allow to merge arbitrary articles, then already increasing the H-index by one is NP-hard. Experiments on Google Scholar profiles of AI researchers show that the H-index can be manipulated substantially only by merging articles with highly dissimilar titles, which would be easy to discover.

We propose a method to guide a Monte Carlo search in the initial moves of the game of Go. Our method matches the current state of a Go board against clusters of board configurations that are derived from a large number of games played by experts. The main advantage of this method is that it does not require an exact match of the current board, and hence is effective for a longer sequence of moves compared to traditional opening books. We apply this method to two different open-source Go-playing programs. Our experiments show that this method, through its filtering or biasing the choice of a next move to a small subset of possible moves, improves play effectively in the initial moves of a game.

Several applications including text-to-speech require some normalized format of non-standard words in various domains such as numbers, dates, and currencies and in various human languages. The traditional approach of manually constructing a program for such a normalization task requires expertise in both programming and target (human) language and further does not scale to a large number of domain, format, and target language combinations. We propose to learn programs for such normalization tasks through examples. We present a domain-specific programming language that offers appropriate abstractions for succinctly describing such normalization tasks, and then present a novel search algorithm that can effectively learn programs in this language from input-output examples. We also briefly describe domain-specific heuristics for guiding users of our system to provide representative examples for normalization tasks related to that domain. Our experiments show that weare able to effectively learn desired programs for a variety of normalization tasks.

Automatic algorithm configurators are important practical tools for improving program performance measures, such as solution time or prediction accuracy. Local search approaches in particular have proven very effective for tuning algorithms. In sequential local search, the use of predictive models has proven beneficial for obtaining good tuning results. We study the use of non-parametric models in the context of population-based algorithm configurators. We introduce a new model designed specifically for the task of predicting high-performance regions in the parameter space. Moreover, we introduce the ideas of genetic engineering of offspring as well as sexual selection of parents. Numerical results show that model-based genetic algorithms significantly improve our ability to effectively configure algorithms automatically.

Unlike other forms of spamming, it is difficult to collect a large amount of gold-standard labels for reviews Many existing methods on review spam detection by means of manual effort. Thus, most of these methods considering text content merely utilize simple text [Mukherjee et al., 2013; Li et al., 2013a; Sun et al., features such as content similarity. We explore a 2013] just rely on the ad-hoc or pseudo fake or non-fake novel idea of exploiting text generality for improving labels for model training, such as the labels annotated by spam detection. Besides, apart from the task the Amazon anonymous online workers [Ott et al., 2011; of review spam detection, although there have also Li et al., 2014]. On the other hand, some unsupervised been some works on identifying the review spammers methods have been proposed to detect the individual review (users) and the manipulated offerings (items), spammer [Mukherjee et al., 2013; Lim et al., 2010; no previous works have attempted to solve these Wang et al., 2011] and review spammer groups [Mukherjee et three tasks in a unified model. We have proposed al., 2012]. In addition, time series pattern [Xie et al., 2012], a unified probabilistic graphical model to detect rating distribution [Feng et al., 2012], reviewer graph [Wang et the suspicious review spams, the review spammers al., 2011], and reviewing burstiness [Fei et al., 2013] have also and the manipulated offerings in an unsupervised been applied to identify the review spams in an unsupervised manner.

We propose a new Bayesian model for reliable aggregatio of crowdsourced estimates of real-valued quantities in participatory sensing applications. Existing approaches focus on probabilistic modelling of user’s reliability as the key to accurate aggregation. However, these are either limited to estimating discrete quantities, or require a significant number of reports from each user to accurately model their reliability. To mitigate these issues, we adopt a community-based approach, which reduces the data required to reliably aggregate real-valued estimates, by leveraging correlations between the reporting behaviour of users belonging to different communities. As a result, our method is up to 16.6% more accurate than existing state-of-the-art methods and is up to 49% more effective under data sparsity when used to estimate Wi-Fi hotspot locations in a real-world crowdsourcing application.