We present JudgeD, a probabilistic datalog. A JudgeD program defines a distribution over a set of traditional datalog programs by attaching logical sentences to clauses to implicitly specify traditional data programs. Through the logical sentences, JudgeD provides a novel method for the expression of complex dependencies between both rules and facts. JudgeD is implemented as a proof-of-concept in the language Python. The implementation allows connection to external data sources, and features both a Monte Carlo probability approximation as well as an exact solver supported by BDDs. Several directions for future work are discussed and the implementation is released under the MIT license.

Emerging AI systems will be making more and more decisions that impact the lives of humans in a significant way. It is essential, then, that these AI systems make decisions that take into account the desires, goals, and preferences of other people, while simultaneously learning about what those preferences are. In this work, we argue that the reinforcement-learning framework achieves the appropriate generality required to theorize about an idealized ethical artificial agent, and offers the proper foundations for grounding specific questions about ethical learning and decision making that can promote further scientific investigation. We define an idealized formalism for an ethical learner, and conduct experiments on two toy ethical dilemmas, demonstrating the soundness and flexibility of our approach. Lastly, we identify several critical challenges for future advancement in the area that can leverage our proposed framework.

In this paper we approach a subset of semantic analysis tasks through a symbiotic cognitive computing approach -- the user and the system learn from each other and accomplish the tasks better than they would do on their own. Our approach starts with a domain expert building a simplified domain model (e.g. semantic lexicons) and annotating documents with that model. The system helps the user by allowing them to obtain quicker results, and by leading them to refine their understanding of the domain. Meanwhile, through the feedback from the user, the system adapts more quickly and produces more accurate results. We believe this virtuous cycle is key for building next generation high quality semantic analysis systems. We present some preliminary findings and discuss our results on four aspects of this virtuous cycle, namely: the intrinsic incompleteness of semantic models, the need for a human in the loop, the benefits of a computer in the loop and finally the overall improvements offered by the human-computer interaction in the process.

In this paper we explain our concept of Interaction and Task Patterns, and discuss how such patterns can be applied to support mixed-initiative in symbiotic human-robot interaction both with service and industrial robotic systems.

We propose a new model for learning bilingual word representations from non-parallel document-aligned data. Following the recent advances in word representation learning, our model learns dense real-valued word vectors, that is, bilingual word embeddings (BWEs). Unlike prior work on inducing BWEs which heavily relied on parallel sentence-aligned corpora and/or readily available translation resources such as dictionaries, the article reveals that BWEs may be learned solely on the basis of document-aligned comparable data without any additional lexical resources nor syntactic information. We present a comparison of our approach with previous state-of-the-art models for learning bilingual word representations from comparable data that rely on the framework of multilingual probabilistic topic modeling (MuPTM), as well as with distributional local context-counting models. We demonstrate the utility of the induced BWEs in two semantic tasks: (1) bilingual lexicon extraction, (2) suggesting word translations in context for polysemous words. Our simple yet effective BWE-based models significantly outperform the MuPTM-based and context-counting representation models from comparable data as well as prior BWE-based models, and acquire the best reported results on both tasks for all three tested language pairs.

Model-Based Diagnosis (MBD) is a principled and domain-independent way of analyzing why a system under examination is not behaving as expected. Given an abstract description (model) of the system's components and their behavior when functioning normally, MBD techniques rely on observations about the actual system behavior to reason about possible causes when there are discrepancies between the expected and observed behavior. Due to its generality, MBD has been successfully applied in a variety of application domains over the last decades. In many application domains of MBD, testing different hypotheses about the reasons for a failure can be computationally costly, e.g., because complex simulations of the system behavior have to be performed. In this work, we therefore propose different schemes of parallelizing the diagnostic reasoning process in order to better exploit the capabilities of modern multi-core computers. We propose and systematically evaluate parallelization schemes for Reiter's hitting set algorithm for finding all or a few leading minimal diagnoses using two different conflict detection techniques. Furthermore, we perform initial experiments for a basic depth-first search strategy to assess the potential of parallelization when searching for one single diagnosis. Finally, we test the effects of parallelizing "direct encodings" of the diagnosis problem in a constraint solver.

The paper focuses on finding the m best solutions to combinatorial optimization problems using best-first or depth-first branch and bound search. Specifically, we present a new algorithm m-A*, extending the well-known A* to the m-best task, and for the first time prove that all its desirable properties, including soundness, completeness and optimal efficiency, are maintained. Since best-first algorithms require extensive memory, we also extend the memory-efficient depth-first branch and bound to the m-best task. We adapt both algorithms to optimization tasks over graphical models (e.g., Weighted CSP and MPE in Bayesian networks), provide complexity analysis and an empirical evaluation. Our experiments confirm theory that the best-first approach is largely superior when memory is available, but depth-first branch and bound is more robust. We also show that our algorithms are competitive with related schemes recently developed for the m-best task.

Hypothetical “value learning” AIs learn human values and then try to act according to those values. The design of such AIs, however, is hampered by the fact that there exists no satisfactory definition of what exactly human values are. After arguing that the standard concept of preference is insufficient as a definition, I draw on reinforcement learning theory, emotion research, and moral psychology to offer an alternative definition. In this definition, human values are conceptualized as mental representations that encode the brain’s value function (in the reinforcement learning sense) by being imbued with a context-sensitive affective gloss. I finish with a discussion of the implications that this hypothesis has on the design of value learners.

Hashtags are useful in many applications, such as tweet classification, clustering, searching, indexing and social network analysis. This study seeks to recommend relevant Twitter hashtags for health-related keywords based on distributed language representations, generated by the state-of-the-art Deep Learning technology. The word embeddings are built from billions of tweet words without supervision. To the best of our knowledge, this is the first study of applying distributed language representations to recommending hashtags for keywords. The experiment showed that this approach outperformed the baseline approach that is based on keyword and hashtag co-occurrence in tweets.

The capability of collaboration is critical in the design of symbiotic cognitive systems. To obtain this functional capability, a cognitive system should possess evaluative and communicative processes. Emotions and their underlying processes provide such functions in social and collaborative environments. We investigate the mutual influence of affective and collaboration processes in a cognitive theory to support the interaction between humans and robots or virtual agents. We have developed new algorithms for these processes, as well as a new overall computational model for implementing collaborative robots and agents. We build primarily on the cognitive appraisal theory of emotions and the SharedPlans theory of collaboration to investigate the structure, fundamental processes and functions of emotions in a collaboration context.