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.

This study investigates the public health intelligence utility of Yik Yak, a social media platform that allows users to anonymously post and view messages within precise geographic locations. Our dataset contains 122,179 “yaks” collected from 120 college campuses across the United States during 2015. We first present an exploratory analysis of the topics commonly discussed in Yik Yak, clarifying the health issues for which this may serve as a source of information. We then present an in-depth content analysis of data describing substance use, an important public health issue that is not often discussed in public social media, but commonly discussed on Yik Yak under the cloak of anonymity.

It is well known that the title of an article impacts how well it is discovered by potential readers and read. With both people and search engines, acting on behalf of people, accessing papers from digital libraries, it is important that the paper titles should promote discovery. In this paper, we investigate the characteristics of titles of AI papers and then propose au- tomatic ways to augment them so that they can be better in- dexed and discovered by users. A user study with researchers shows that they overwhelmingly prefer the augmented titles over the originals for being more helpful.

The general public shows great potential for utilizing scientific research. For example, a singer discovered her ectopic pregnancy by looking up clinical case reports. However, an exorbitant paywall impedes the public’s access to scientific literature. Our case study on a social network demonstrates a growing need for non-open access publications, especially for biomedical literature. The challenge is that non-open access papers are protected by copyright licenses that bar free distribution. In this paper, we propose a technical framework that leverages the doctrine of "idea-expression dichotomy" to bring ideas across paywalls. Idea-expression dichotomy prevents copyright holders from monopolizing ideas, theories, facts, and concepts. Therefore facts may pass through paywalls unencumbered by copyright license restrictions. Existing fact extraction methods (such as information extraction) require either large training sets or domain knowledge, which is intractable for the diverse biomedical scope spanning from clinical findings to genomics. We therefore develop a rule-based system to represent and extract facts. Social networkers and academics validated the effectiveness of our approach. 7 out of 9 users rated the paper’s information from the facts to be above average (≥6/10). Only 7% of the extracted facts were rated misleading.

We present a framework that enables online planning for robotic systems in dynamic environments. The PLANrm framework presented in this work utilizes the theory of robustness and monitoring of Metric Temporal Logic (MTL) specifications to inspect and modify available plans to both avoid obstacles and satisfy specifications in a dynamic environment. The use of MTL allows the practitioner to set complex event and timing based specifications that need to be satisfied in the execution of the plan. The monitoring algorithm inspects the possible paths in a bounded window and selects and adjusts a path to satisfy the specifications. In this paper, we present initial results on the framework and an extended summary of the algorithmic results. The approach is illustrated using a running example of a car-like model with a number of MTL specifications.

This paper describes Hy-CIRCA, an architecture for verified, correct-by-construction planning and execution for hy- brid systems, including non-linear continuous dynamics. Hy-CIRCA addresses the high computational complexity of such systems by first planning at an abstract level, and then progressively refining the original plan. Hy-CIRCA is an extension of our Playbook approach, which aims to make it easy for users to exert supervisory control over multiple autonomous systems by “calling a play.” The Playbook approach is implemented by combining (1) a human-machine interface for commanding and monitoring the autonomous systems; (2) a hierarchical planner for translating commands into executable plans; and (3) a smart executive to manage plan execution by coordinating the control systems of the individual autonomous agents, tracking plan execution, and triggering replanning when necessary. Hy-CIRCA integrates the dReal non-linear SMT solver, with enhanced versions of the SHOP2 HTN planner and the CIRCA Controller Synthesis Module (CSM). Hy-CIRCA’s planning process has 5 steps: (1) Using SHOP2, compute an approximate mission plan. While computing this plan, compute a hybrid automaton model of the plan, featuring more expressive continuous dynamics. (2) Using dReal, solve this hybrid model, establishing the correctness of the plan, and computing values for its continuous parameters. To execute the plan, (3) extract from the plan specifications for closed-loop, hard real-time supervisory controllers for the agents that must execute the plan. (4) Based upon these specifications, use the CIRCA CSM to plan the controllers. To ensure correct execution, (5) verify the CSM-generated controllers with dReal.

Hybrid planning with nonlinear continuous change is a significant challenge for existing planners. Prior works limit their scope to linear change or base their formalisms in model checking frameworkswith inherent limitations. We address nonlinear PDDL+ planning with anew encoding in first order logic over real valued functions. Our planner, PluReal, translates PDDL+ to this logical encoding and applies the dReal Satisfiability Modulo Theories (SMT) solver to construct plans. Unlike prior work that uses dReal in the hybrid system model checking tradition, PluReal is based in the planning as satisfiability (SAT) heritage. Adopting the SAT approach helps lift several unnatural restrictions that are imposed by the translation through hybrid systems and leads to improved scalability even without SMT solver variable selection heuristics.

The hospital readmission rate of patients within 30 days after discharge is broadly accepted as a healthcare quality measure and cost driver in the United States. The ability to estimate hospitalization costs alongside 30 day risk-stratification for such readmissions provides additional benefit for accountable care, now a global issue and foundation for the U.S.~government mandate under the Affordable Care Act. Recent data mining efforts either predict healthcare costs or risk of hospital readmission, but not both. In this paper we present a dual predictive modeling effort that utilizes healthcare data to predict the risk and cost of any hospital readmission (``all-cause''). For this purpose, we explore machine learning algorithms to do accurate predictions of healthcare costs and risk of 30-day readmission.Results on risk prediction for ``all-cause'' readmission compared to the standardized readmission tool (LACE) are promising, and the proposed techniques for cost prediction consistently outperform baseline models and demonstrate substantially lower mean absolute error (MAE).