Geometry reasoning and proof form a major and challenging component in the K-121 mathematics curriculum. Although several computerized systems exist that help students learn and practice general geometry concepts, they do not target geometry proof problems, which are more advanced and difficult. Powerful geometry theorem provers also exist, however they typically employ advanced algebraic methods and generate complex, difficult to understand proofs, and thus do not meet general K-12 students’ educational needs. This paper tackles these weaknesses of prior systems by introducing a geometry proof system, iGeoTutor, capable of generating human-readable elementary proofs, i.e. proofs using standard Euclidean axioms. We have gathered 77 problems in total from various sources, including ones unsolvable by other systems and from Math competitions. iGeoTutor solves all but two problems in under two minutes each, and more importantly, demonstrates a much more effective and intelligent proof search than prior systems. We have also conducted a pilot study with 12 high school students, and the results show that iGeoTutor provides a clear benefit in helping students learn geometry proofs. We are in active discussions with Khan Academy and local high schools for possible adoption of iGeo-Tutor in real learning environments.

We consider a setting where a team of humans oversee the coordination of multiple Unmanned Aerial Vehicles (UAVs) to perform a number of search tasks in dynamic environments that may cause the UAVs to drop out. Hence, we develop a set of multi-UAV supervisory control interfaces and a multi-agent coordination algorithm to support human decision making in this setting. To elucidate the resulting interactional issues, we compare manual and mixed-initiative task allocation in both static and dynamic environments in lab studies with 40 participants and observe that our mixed-initiative system results in lower workloads and better performance in re-planning tasks than one which only involves manual task allocation. Our analysis points to new insights into the way humans appropriate flexible autonomy.

We contribute a novel approach to understand, dialogue, plan, and execute complex sentences to command a mobile service robot. We define a complex command as a natural language sentence consisting of sensing-based conditionals, conjunctions, and disjunctions. We introduce a flexible template-based algorithm to extract such structure from the parse tree of the sentence. As the complexity of the command increases, extracting the right structure using the template-based algorithm decreases becomes more problematic. We introduce two different dialogue approaches that enable the user to confirm or correct the extracted command structure. We present how the structure used to represent complex commands can be directly used for planning and execution by the service robot. We show results on a corpus of 100 complex commands

In this paper, we adopt a multimodal emotion recognition framework by combining eye movements and electroencephalography (EEG) to enhance emotion recognition. The main contributions of this paper are twofold. a) We investigate sixteen eye movements related to emotions and identify the intrinsic patterns of these eye movements for three emotional states: positive, neutral and negative. b) We examine various modality fusion strategies for integrating users external subconscious behaviors and internal cognitive states and reveal that the characteristics of eye movements and EEG are complementary to emotion recognition. Experiment results demonstrate that modality fusion could significantly improve emotion recognition accuracy in comparison with single modality. The best accuracy achieved by fuzzy integral fusion strategy is 87.59%, whereas the accuracies of solely using eye movements and EEG data are 77.80% and 78.51%, respectively.

Computer-aided translation (CAT) system is the most popular tool which helps human translators perform language translation efficiently. To further improve the efficiency, there is an increasing interest in applying the machine translation (MT) technology to upgrade CAT. Post-editing is a standard approach: human translators generate the translation by correcting MT outputs. In this paper, we propose a novel approach deeply integrating MT into CAT systems: a well-designed input method which makes full use of the knowledge adopted by MT systems, such as translation rules, decoding hypotheses and n-best translation lists. Our proposed approach allows human translators to focus on choosing better translation results with less time rather than just complete translation themselves. The extensive experiments demonstrate that our method saves more than 14% time and over 33% keystrokes, and it improves the translation quality as well by more than 3 absolute BLEU scores compared with the strong baseline, i.e., post-editing using Google Pinyin.

The recent landmark "right to be forgotten" ruling by the EU Court gives EU citizens the right to remove certain links that are "inaccurate, inadequate, irrelevant or excessive" from search results under their names. While we agree with the spirit of the ruling — to empower individuals to manage their personal data while keeping a balance between such right and the freedom of expression, we believe that the ruling is impractical as it provides neither precise criteria for evaluating removal requests nor concrete guidelines for implementation. Consequently, Google's current implementation has several problems concerning scalability, objectivity, and responsiveness. Instead of the right to be forgotten, we propose the right to obscure certain facts about oneself on search engines, and a simple mechanism which respects the spirit of the ruling by giving people more power to influence search results for queries on their names. Specifically, under our proposed mechanism, data subjects will be able to register minus terms, and search results for their name queries that contain such terms would be filtered out. We implement a proof-of-concept search engine following the proposed mechanism, and conduct experiments to explore the influences it might have on users' impressions on different data subjects.

Given a class of students, and a pool of questions in the domain of study, what subset will constitute a good exam? Millions of educators are dealing with this difficult problem worldwide, yet exams are still composed manually in non-systematic ways. In this work we present a novel algorithmic framework for exam composition. Our framework requires two input components: a student population represented by a distribution over overlay models, each consisting of a set of mastered abilities, or actions; and a target model ordering that, given any two student models, defines which should be given the higher grade. To determine the performance of a student model on a potential question, we test whether it satisfies a disjunctive action landmark, i.e., whether its abilities are sufficient to follow at least one solution path. We present a novel utility function for evaluating exams, using the described components. An exam is highly evaluated if it is expected to order the student population with high correlation to the target order.The merit of our algorithmic framework is exemplified with real auto-generated questions in the domain of middle-school algebra.

In this paper we investigate the model-checking problem of pushdown multi-agent systems for ATL* specifications.To this aim, we introduce pushdown game structures over which ATL* formulas are interpreted. We show an algorithm that solves the addressed model-checking problem in 3ExpTime. We also provide a 2ExpSpace lower bound by showing a reduction from the word acceptance problem for deterministic Turing machines with doubly exponential space.

We investigate the general problem of establishing whether a swarm satisfies an emergent property. We put forward a formal model for swarms that accounts for their nature of unbounded collections of agents following simple local protocols. We formally define the decision problem of determining whether a swarm satisfies an emergent property. We introduce a sound and complete procedure for solving the problem. We illustrate the technique by applying it to the Beta aggregation algorithm.

RB+-ATL is an extension of ATL where it is possible to model consumption and production of several resources by a set of agents. The model-checking problem for RB+-ATL is known to be decidable. However the only available model-checking algorithm for RB+-ATL uses a forward search of the state space, and hence does not have an efficient symbolic implementation. In this paper, we consider a fragment of RB+-ATL, 1RB+-ATL, that allows only one resource type. We give a symbolic model-checking algorithm for this fragment of RB+-ATL, and evaluate the performance of an MCMAS-based implementation of the algorithm on an example problem that can be scaled to large state spaces.