We investigate the task of generating coherent survey articles for scientific topics. We introduce an extractive summarization algorithm that combines a content model with a discourse model to generate coherent and readable summaries of scientific topics using text from scientific articles relevant to the topic. Human evaluation on 15 topics in computational linguistics shows that our system produces significantly more coherent summaries than previous systems. Specifically, our system improves the ratings for coherence by 36% in human evaluation compared to C-Lexrank, a state of the art system for scientific article summarization.

In this paper, we design a distributed mechanism that is able to create a social convention within a large convention space for multiagent systems (MAS) operating on various topologies. Specifically, we investigate a language coordination problem in which agents in a dynamic MAS construct a common lexicon in a decentralized fashion. Agent interactions are modeled using a language game where every agent repeatedly plays with its neighbors. Each agent stochastically updates its lexicons based on the utility values of the received lexicons from its immediate neighbors. We present a novel topology-aware utility computation mechanism and equip the agents with the ability to reorganize their neighborhood based on this utility estimate to expedite the convention formation process. Extensive simulation results indicate that our proposed mechanism is both effective (able to converge into a large majority convention state with more than 90\% agents sharing a high-quality lexicon) and efficient (faster) as compared to state-of-the-art approaches for social conventions in large convention spaces.

In many cases, creating long-term solutions to sustainability issues requires not only innovative technology, but also large-scale public adoption of the proposed solutions. Social simulations are a valuable but underutilized tool that can help public policy researchers understand when sustainable practices are likely to make the delicate transition from being an individual choice to becoming a social norm. In this paper, we introduce a new normative multi-agent architecture, Cognitive Social Learners (CSL), that models bottom-up norm emergence through a social learning mechanism, while using BDI (Belief/Desire/Intention) reasoning to handle adoption and compliance. CSL preserves a greater sense of cognitive realism than influence propagation or infectious transmission approaches, enabling the modeling of complex beliefs and contradictory objectives within an agent-based simulation. In this paper, we demonstrate the use of CSL for modeling norm emergence of recycling practices and public participation in a smoke-free campus initiative.

Reputation-based approaches allow a crowdsourcing system to identify reliable workers to whom tasks can be delegated. In crowdsourcing systems that can be modeled as multi-agent trust networks consist of resource constrained trustee agents (i.e., workers), workers may need to further sub-delegate tasks to others if they determine that they cannot complete all pending tasks before the stipulated deadlines. Existing reputation-based decision-making models cannot help workers decide when and to whom to sub-delegate tasks. In this paper, we proposed a reputation aware task sub-delegation (RTS) approach to bridge this gap. By jointly considering a worker's reputation, workload, the price of its effort and its trust relationships with others, RTS can be implemented as an intelligent agent to help workers make sub-delegation decisions in a distributed manner. The resulting task allocation maximizes social welfare through efficient utilization of the collective capacity of a crowd, and provides provable performance guarantees. Experimental comparisons with state-of-the-art approaches based on the Epinions trust network demonstrate significant advantages of RTS under high workload conditions.

Deciding the outcome of an election when voters have provided only partial orderings over their preferences requires voting rules that accommodate missing data. While existing techniques, including considerable recent work, address missingness through circumvention, we propose the novel application of conventional machine learning techniques to predict the missing components of ballots via latent patterns in the information that voters are able to provide. We show that suitable predictive features can be extracted from the data, and demonstrate the high performance of our new framework on the ballots from many real world elections, including comparisons with existing techniques for voting with partial orderings. Our technique offers a new and interesting conceptualization of the problem, with stronger connections to machine learning than conventional social choice techniques.

This paper presents a novel approach to inductive process modeling, the task of constructing a quantitative account of dynamical behavior from time-series data and background knowledge. We review earlier work on this topic, noting its reliance on methods that evaluate entire model structures and use repeated simulation to estimate parameters, which together make severe computational demands. In response, we present an alternative method for process model induction that assumes each process has a rate, that this rate is determined by an algebraic expression, and that changes due to a process are directly proportionalto its rate. We describe RPM, an implemented system that incorporates these ideas, and we report analyses and experiments that suggest it scales well to complex domains and data sets. In closing, we discuss related research and outline ways to extend the framework.

Community-based Question Answering (CQA) has become popular in knowledge sharing sites since it allows users to get answers to complex, detailed, and personal questions directly from other users. Large archives of historical questions and associated answers have been accumulated. Retrieving relevant historical answers that best match a question is an essential component of a CQA service. Most state of the art approaches are based on bag-of-words models, which have been proven successful in a range of text matching tasks, but are insufficient for capturing the important word sequence information in short text matching. In this paper, a new architecture is proposed to more effectively model the complicated matching relations between questions and answers. It utilises a similarity matrix which contains both lexical and sequential information. Afterwards the information is put into a deep architecture to find potentially suitable answers. The experimental study shows its potential in improving matching accuracy of question and answer.

Integrating wind power into the grid is challenging because of its random nature. Integration is facilitated with accurate short-term forecasts of wind power. The paper presents a spatio-temporal wind speed forecasting algorithm that incorporates the time series data of a target station and data of surrounding stations. Inspired by Compressive Sensing (CS) and structured-sparse recovery algorithms, we claim that there usually exists an intrinsic low-dimensional structure governing a large collection of stations that should be exploited. We cast the forecasting problem as recovery of a block-sparse signal $\boldsymbol{x}$ from a set of linear equations $\boldsymbol{b} = A\boldsymbol{x}$ for which we propose novel structure-sparse recovery algorithms. Results of a case study in the east coast show that the proposed Compressive Spatio-Temporal Wind Speed Forecasting (CST-WSF) algorithm significantly improves the short-term forecasts compared to a set of widely-used benchmark models.

Sophisticated autonomous AI may need to base its behavior on fuzzy concepts such as well-being or rights. These concepts cannot be given an explicit formal definition, but obtaining desired behavior still requires a way to instill the concepts in an AI system. To solve the problem, we review evidence suggesting that the human brain generates its concepts using a relatively limited set of rules and mechanisms. This suggests that it might be feasible to build AI systems that use similar criteria for generating their own concepts, and could thus learn similar concepts as humans do. Major challenges to this approach include the embodied nature of human thought, evolutionary vestiges in cognition, the social nature of concepts, and the need to compare conceptual representations between humans and AI systems.

Systems biology, the study of the intricate, ramified, com-plex and interacting mechanisms underlying life, often proves too complex for unaided human understanding, even by groups of people working together. This difficulty is ex-acerbated by the high volume of publications in molecular biology. The Big C (‘C’ for Cyc) is a system designed to (semi-)automatically acquire, integrate, and use complex mechanism models, specifically related to cancer biology, via automated reading and a hyper-detailed refinement pro-cess resting on Cyc’s logical representations and powerful inference mechanisms. We aim to assist cancer research and treatment by achieving elements of biologist-level reason-ing, but with the scale and attention to detail that only com-puter implementations can provide.