Computer Science (CS) education is critical in todays world, and introductory programming courses are considered extremely difficult and frustrating, often considered a major stumbling block for students willing to pursue computer programming related careers. In this paper, we describe the design of Socratic Tutor, an Intelligent Tutoring System that can help novice programmers to better understand programming concepts. The system was inspired by the Socratic method of teaching in which the main goal is to ask a set of guiding questions about key concepts and major steps or segments of complete code examples. To evaluate the Socratic Tutor, we conducted a pilot study with 34 computer science students and the results are promising in terms of learning gains.

Navigating a career constitutes one of life’s most enduring challenges, particularly within a unique organization like the US Navy. While the Navy has numerous resources for guidance, accessing and identifying key information sources across the many existing platforms can be challenging for sailors (e.g., determining the appropriate program or point of contact, developing an accurate understanding of the process, and even recognizing the need for planning itself). Focusing on intermediate goals, evaluations, education, certifications, and training is quite demanding, even before considering their cumulative long-term implications. These are on top of generic personal issues, such as financial difficulties and homesickness when at sea for prolonged periods. We present the preliminary construction of a conversational intelligent agent designed to provide a user-friendly, adaptive environment that recognizes user input pertinent to these issues and provides guidance to appropriate resources within the Navy. User input from “counseling sessions” is linked, using advanced natural language processing techniques, to our framework of Navy training and education standards, promotion protocols, and organizational structure, producing feedback on resources and recommendations sensitive to user history and stated career goals. The proposed innovative technology monitors sailors’ career progress, proactively triggering sessions before major career milestones or when performance drops below Navy expectations, by using a mixed-initiative design. System-triggered sessions involve positive feedback and informative dialogues (using existing Navy career guidance protocols). The intelligent agent also offers counseling for personal problems, triggering targeted dialogues designed to gather more information, offer tailored suggestions, and provide referrals to appropriate resources or to a human counselor when in-depth counseling is warranted. This software, currently in alpha testing, has the potential to serve as a centralized information hub, engaging and encouraging sailors to take ownership of their career paths in the most efficient way possible, benefiting both individuals and the Navy as a whole.

The Thirty-First International Florida Artificial Intelligence Research Society Conference (FLAIRS-31) was held May 21-23, 2018, at the Crowne Plaza Oceanfront in Melbourne, Florida, USA. The conference events included invited speakers, special tracks, and presentations of papers, posters, and awards. The conference chair was Zdravko Markov from Central Connecticut State University. The program co-chairs were Vasile Rus from the University of Memphis and Keith Brawner from the Army Research Laboratory. The special tracks were coordinated by Roman Barták from Charles University in Prague.

Learners’ skills decay during gaps in instruction, since they lack the structure and motivation to continue studying. To meet this challenge, the PAL3 system was designed to accompany a learner throughout their career and mentor them to build and maintain skills through: 1) the use of an embodied pedagogical agent (Pal), 2) a persistent learning record that drives a student model which estimates forgetting, 3) an adaptive recommendation engine linking to both intelligent tutors and traditional learning resources, and 4) game-like mechanisms to promote engagement (e.g., leaderboards, effort-based point rewards, unlocking customizations). The design process for PAL3 is discussed, from the perspective of insights and revisions based on a series of formative feedback and evaluation sessions.

We present an unsupervised approach for abnormal event detection in videos. We propose, given a dictionary of features learned from local spatiotemporal cuboids using the sparse coding objective, the abnormality of an event depends jointly on two factors: the frequency of each feature in reconstructing all events (or, rarity of a feature) and the strength by which it is used in reconstructing the current event (or, the absolute coefficient). The Incremental Coding Length (ICL) of a feature is a measure of its entropy gain. Given a dictionary, the ICL computation does not involve any parameter, is computationally efficient and has been used for saliency detection in images with impressive results. In this paper, the rarity of a dictionary feature is learned online as its average energy, a function of its ICL. The proposed approach is applicable to real world streaming videos. Experiments on three benchmark datasets and evaluations in comparison with a number of mainstream algorithms show that the approach is comparable to the state-of-the-art.

Past research on grammar induction has found promising results in predicting parts-of-speech from n-grams using a fixed vocabulary and a fixed context. In this study, we investigated grammar induction whereby we varied vocabulary size and context size. Results indicated that as context increased for a fixed vocabulary, overall accuracy initially increased but then leveled off. Importantly, this increase in accuracy did not occur at the same rate across all syntactic categories. We also address the dynamic relation between context and vocabulary in terms of grammar induction in an unsupervised methodology. We formulate a model that represents a relationship between vocabulary and context for grammar induction. Our results concur with what has been called the word spurt phenomenon in the child language acquisition literature.

Text summarization is an important field in the area of natural language processing and text mining. This paper proposes an extraction-based model which uses graph-based and information theoretic concepts for multi-document summarization. Our method constructs a directed weighted graph from the original text by adding a vertex for each sentence, and compute a weighted edge between sentences which is based on distortion measures. In this paper we proposed a combination of these two models by representing the input as a graph, using distortion measures as the weight function and a ranking algorithm. Finally, a ranking algorithm is applied to identify the most important sentences to be included in the summary. By defining a proper distortion measure and ranking algorithm, this model gains promising results on the DUC2002 which is a well known real world data set. The results and ROUGE-1 scores of our model is fairly close to other successful models.

This paper shows that the combination of knowledge and corpus-based word-to-word similarity measures can produce higher agreement with human judgment than any of the in-dividual measures. While this might be a predictable result, the paper provides insights about the circumstances under which a combination is productive and about the improve-ment levels that are to be expected. The experiments presented here were conducted using the word-to-word similarity measures included in SEMILAR, a freely available semantic similarity toolkit.

Google Translate provides a multilingual machine-translation service by automatically translating one written language to another. Google translate is allegedly limited in its accuracy in translation, however. This study investigated the accuracy of Google Chinese-to-English translation from the perspectives of formality and cohesion with two comparisons: Google translation with human expert translation, and Google translation with Chinese source language. The text sample was a collection of 289 spoken and written texts excerpts from the Selected Works of Mao Zedong in both Chinese and English versions. Google translate was used to translate the Chinese texts into English. These texts were analyzed by the automated text analysis tools: the Chinese and English LIWC, and the Chinese and English Coh-Metrix. Results of Pearson correlations on formality and cohesion showed Google English translation was highly correlated with both human English translation and the original Chinese texts.

The two common design principles for agent-based models, KISS (Keep It Simple, Stupid) and KIDS (Keep It Descriptive, Stupid) offer limited traction for developing cognitive agents, who typically have strong ties to research findings and established theories of cognition. A KIKS principle (Keep It Knowledgeable, Stupid) is proposed to capture the fact that cognitive agents are grounded in published research findings and theory, rather than simply selecting parameters in an ad-hoc way. In short, KIKS suggests that modelers should not focus on how many parameters, but should instead focus on choosing the right research papers and implement each of their key parameters and mechanisms. Based on this principle, a design process for creating cognitive agents based on cognitive models is proposed. This process is centered around steps that cognitive agent designers are already consider (e.g., literature search, validation, implementing a computational model). However, the KIKS process suggests two differences. First, KIKS calls for reporting explicit metadata on the empirical and theoretical relationships that an agent's cognitive model is intended to capture. Each such relationship should be associated with a published paper that supports it. This metadata would serve a valuable purpose for comprehending, validating, and comparing the cognitive models used by different agents. Second, KIKS calls for validation tests to be specified before creating an agent's cognitive model computationally. This process, known as test-driven design, can be used to monitor the adherence of a cognitive agent to its underlying knowledge base as it evolves through different versions. Implications, advantages, and limitations of the proposed process for KIKS are discussed.