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.

Many scenarios require that robots work together as a team in order to effectively accomplish their tasks. However, pre-coordinating these teams may not always be possible given the growing number of companies and research labs creating these robots. Therefore, it is desirable for robots to be able to reason about ad hoc teamwork and adapt to new teammates on the fly. Past research on ad hoc teamwork has focused on relatively simple domains, but this paper demonstrates that agents can reason about ad hoc teamwork in complex scenarios. To handle these complex scenarios, we introduce a new algorithm, PLASTIC–Policy, that builds on an existing ad hoc teamwork approach. Specifically, PLASTIC– Policy learns policies to cooperate with past teammates and reuses these policies to quickly adapt to new teammates. This approach is tested in the 2D simulation soccer league of RoboCup using the half field offense task.

Online, sample-based planning algorithms for POMDPs have shown great promise in scaling to problems with large state spaces, but they become intractable for large action and observation spaces. This is particularly problematic in multiagent POMDPs where the action and observation space grows exponentially with the number of agents. To combat this intractability, we propose a novel scalable approach based on sample-based planning and factored value functions that exploits structure present in many multiagent settings. This approach applies not only in the planning case, but also in the Bayesian reinforcement learning setting. Experimental results show that we are able to provide high quality solutions to large multiagent planning and learning problems.

Many multiagent applications require an agent to learn quickly how to interact with previously unknown other agents. To address this problem, researchers have studied learning algorithms which compute posterior beliefs over a hypothesised set of policies, based on the observed actions of the other agents. The posterior belief is complemented by the prior belief, which specifies the subjective likelihood of policies before any actions are observed. In this paper, we present the first comprehensive empirical study on the practical impact of prior beliefs over policies in repeated interactions. We show that prior beliefs can have a significant impact on the long-term performance of such methods, and that the magnitude of the impact depends on the depth of the planning horizon. Moreover, our results demonstrate that automatic methods can be used to compute prior beliefs with consistent performance effects. This indicates that prior beliefs could be eliminated as a manual parameter and instead be computed automatically.

Recently, solving rank minimization problems by leveraging nonconvex relaxations has received significant attention. Some theoretical analyses demonstrate that it can provide a better approximation of original problems than convex relaxations. However, designing an effective algorithm to solve nonconvex optimization problems remains a big challenge. In this paper, we propose an Iterative Shrinkage-Thresholding and Reweighted Algorithm (ISTRA) to solve rank minimization problems using the nonconvex weighted nuclear norm as a low rank regularizer. We prove theoretically that under certain assumptions our method achieves a high-quality local optimal solution efficiently. Experimental results on synthetic and real data show that the proposed ISTRA algorithm outperforms state-of-the-art methods in both accuracy and efficiency.

In multi-label learning, an example is represented by a descriptive feature associated with several labels. Simply considering labels as independent or correlated is crude; it would be beneficial to define and exploit the causality between multiple labels. For example, an image label 'lake' implies the label 'water', but not vice versa. Since the original features are a disorderly mixture of the properties originating from different labels, it is intuitive to factorize these raw features to clearly represent each individual label and its causality relationship.Following the large-margin principle, we propose an effective approach to discover the causal features of multiple labels, thus revealing the causality between labels from the perspective of feature. We show theoretically that the proposed approach is a tight approximation of the empirical multi-label classification error, and the causality revealed strengthens the consistency of the algorithm. Extensive experimentations using synthetic and real-world data demonstrate that the proposed algorithm effectively discovers label causality, generates causal features, and improves multi-label learning.

Neuroimage analysis usually involves learning thousands or even millions of variables using only a limited number of samples. In this regard, sparse models, e.g. the lasso, are applied to select the optimal features and achieve high diagnosis accuracy. The lasso, however, usually results in independent unstable features. Stability, a manifest of reproducibility of statistical results subject to reasonable perturbations to data and the model (Yu 2013), is an important focus in statistics, especially in the analysis of high dimensional data. In this paper, we explore a nonnegative generalized fused lasso model for stable feature selection in the diagnosis of Alzheimer's disease. In addition to sparsity, our model incorporates two important pathological priors: the spatial cohesion of lesion voxels and the positive correlation between the features and the disease labels. To optimize the model, we propose an efficient algorithm by proving a novel link between total variation and fast network flow algorithms via conic duality. Experiments show that the proposed nonnegative model performs much better in exploring the intrinsic structure of data via selecting stable features compared with other state-of-the-arts.

Is it possible to maximize a monotone submodular function faster than the widely used lazy greedy algorithm (also known as accelerated greedy), both in theory and practice? In this paper, we develop the first linear-time algorithm for maximizing a general monotone submodular function subject to a cardinality constraint. We show that our randomized algorithm, STOCHASTIC-GREEDY, can achieve a (1 − 1/e − ε) approximation guarantee, in expectation, to the optimum solution in time linear in the size of the data and independent of the cardinality constraint. We empirically demonstrate the effectiveness of our algorithm on submodular functions arising in data summarization, including training large-scale kernel methods, exemplar-based clustering, and sensor placement. We observe that STOCHASTIC-GREEDY practically achieves the same utility value as lazy greedy but runs much faster. More surprisingly, we observe that in many practical scenarios STOCHASTIC-GREEDY does not evaluate the whole fraction of data points even once and still achieves indistinguishable results compared to lazy greedy.

Matching and merging data from conflicting sources is the bread and butter of data integration, which drives search verticals, e-commerce comparison sites and cyber intelligence. Schema matching lifts data integration - traditionally focused on well-structured data - to highly heterogeneous sources. While schema matching has enjoyed significant success in matching data attributes, inconsistencies can exist at a deeper level, making full integration difficult or impossible. We propose a more fine-grained approach that focuses on correspondences between the values of attributes across data sources. Since the semantics of attribute values derive from their use and co-occurrence, we argue for the suitability of canonical correlation analysis (CCA) and its variants. We demonstrate the superior statistical and computational performance of multiple sparse CCA compared to a suite of baseline algorithms, on two datasets which we are releasing to stimulate further research. Our crowd-annotated data covers both cases that are relatively easy for humans to supply ground-truth, and that are inherently difficult for human computation.

Sampling repeated clinical laboratory tests with appropriate timing is challenging because the latent physiologic function being sampled is in general nonstationary. When ordering repeated tests, clinicians adopt various simple strategies that may or may not be well suited to the behavior of the function. Previous research on this topic has been primarily focused on cost-driven assessments of oversampling. But for monitoring physiologic state or for retrospective analysis, undersampling can be much more problematic than oversampling. In this paper we analyze hundreds of observation sequences of four different clinical laboratory tests to provide principled, data-driven estimates of undersampling and oversampling, and to assess whether the sampling adapts to changing volatility of the latent function. To do this, we developed a new method for fitting a Gaussian process to samples of a nonstationary latent function. Our method includes an explicit estimate of the latent function's volatility over time, which is deterministically related to its nonstationarity. We find on average that the degree of undersampling is up to an order of magnitude greater than oversampling, and that only a small minority are sampled with an adaptive strategy.