The flourishing of online labor markets such as Amazon Mechanical Turk (MTurk) makes it easy to recruit many workers for solving small tasks. We study whether information elicitation and aggregation over a combinatorial space can be achieved by integrating small pieces of potentially imprecise information, gathered from a large number of workers through simple, one-shot interactions in an online labor market. We consider the setting of predicting the ranking of $n$ competing candidates, each having a hidden underlying strength parameter. At each step, our method estimates the strength parameters from the collected pairwise comparison data and adaptively chooses another pairwise comparison question for the next recruited worker. Through an MTurk experiment, we show that the adaptive method effectively elicits and aggregates information, outperforming a naive method using a random pairwise comparison question at each step.

For the learning-to-ranking algorithms used in commercial search engines, a conventional way to generate the training examples is to employ professional annotators to label the relevance of query-url pairs. Since label quality depends on the expertise of annotators to a large extent, this process is time-consuming and labor-intensive. Automatically generating labels from click-through data has been well studied to have comparable or better performance than human judges. Click-through data present users’ action and imply their satisfaction on search results, but exclude the interactions between users and search results beyond the page-view level (e.g., eye and mouse movements). This paper proposes a novel approach to comprehensively consider the information underlying mouse trajectory and click-through data so as to describe user behaviors more objectively and achieve a better understanding of the user experience. By integrating multi-sources data, the proposed approach reveals that the relevance labels of query-url pairs are related to positions of urls and users’ behavioral features. Based on their correlations, query-url pairs can be labeled more accurately and search results are more satisfactory to users. The experiments that are conducted on the most popular Chinese commercial search engine (Baidu) validated the rationality of our research motivation and proved that the proposed approach outperformed the state-of-the-art methods.

A key challenge in developing efficient local search solvers is to effectively minimise search stagnation (i.e. avoiding traps or local minima). A majority of the state-of-the-art local search solvers perform random and/or Novelty-based walks to overcome search stagnation. Although such strategies are effective in diversifying a search from its current local minimum, they do not actively prevent the search from visiting previously encountered local minima. In this paper, we propose a new preventative strategy to effectively minimise search stagnation using pseudo-conflict learning. We define a pseudo-conflict as a derived path from the search trajectory that leads to a local minimum. We then introduce a new variable selection scheme that penalises variables causing those pseudo-conflicts. Our experimental results show that the new preventative approach significantly improves the performance of local search solvers on a wide range of structured and random benchmarks.

Abduction belongs to the most fundamental reasoning methods. It is a method for reverse inference, this means one is interested in explaining observed behavior by finding appropriate causes. We study logic-based abduction, where knowledge is represented by propositional formulas. The computational complexity of this problem is highly intractable in many interesting settings. In this work we therefore present an extensive parameterized complexity analysis of abduction within various fragments of propositional logic together with (combinations of) natural parameters.

We consider an infinite mixture model of Gaussian processes that share mixture components between non-local clusters in data. Meeds and Osindero (2006) use a single Dirichlet process prior to specify a mixture of Gaussian processes using an infinite number of experts. In this paper, we extend this approach to allow for experts to be shared non-locally across the input domain. This is accomplished with a hierarchical double Dirichlet process prior, which builds upon a standard hierarchical Dirichlet process by incorporating local parameters that are unique to each cluster while sharing mixture components between them. We evaluate the model on simulated and real data, showing that sharing Gaussian process components non-locally can yield effective and useful models for richly clustered non-stationary, non-linear data.

In order for robots to intelligently perform tasks with humans, they must be able to access a broad set of background knowledge about the environments in which they operate. Unlike other approaches, which tend to manually define the knowledge of the robot, our approach enables robots to actively query the World Wide Web (WWW) to learn background knowledge about the physical environment. We show that our approach is able to search the Web to infer the probability that an object, such as a "coffee,'' can be found in a location, such as a "kitchen.'' Our approach, called ObjectEval, is able to dynamically instantiate a utility function using this probability, enabling robots to find arbitrary objects in indoor environments. Our experimental results show that the interactive version of ObjectEval visits 28% fewer locations than the version trained offline and 71% fewer locations than a baseline approach which uses no background knowledge.

The Sentential Decision Diagram (SDD) is a recently proposed representation of Boolean functions, containing Ordered Binary Decision Diagrams (OBDDs) as a distinguished subclass. While OBDDs are characterized by total variable orders, SDDs are characterized by dissections of variable orders, known as vtrees. Despite this generality, SDDs retain a number of properties, such as canonicity and a polytime apply operator, that have been critical to the practical success of OBDDs. Moreover, upper bounds on the size of SDDs were also given, which are tighter than comparable upper bounds on the size of OBDDs. In this paper, we analyze more closely some of the theoretical properties of SDDs and their size. In particular, we consider the impact of basing decisions on sentences (using dissections as in SDDs), in comparison to basing decisions on variables (using total variable orders as in OBDDs). Here, we identify a class of Boolean functions where basing decisions on sentences using dissections of a variable order can lead to exponentially more compact SDDs, compared to OBDDs based on the same variable order. Moreover, we identify a fundamental property of the decompositions that underlie SDDs and use it to show how certain changes to a vtree can also lead to exponential differences in the size of an SDD.

In recent times, crowdsourcing over social networks has emerged as an active tool for complex task execution. In this paper, we address the problem faced by a planner to incentivize agents in the network to execute a task and also help in recruiting other agents for this purpose. We study this mechanism design problem under two natural resource optimization settings: (1) cost critical tasks, where the planner's goal is to minimize the total cost, and (2) time critical tasks, where the goal is to minimize the total time elapsed before the task is executed. We define a set of fairness properties that should be ideally satisfied by a crowdsourcing mechanism. We prove that no mechanism can satisfy all these properties simultaneously. We relax some of these properties and define their approximate counterparts. Under appropriate approximate fairness criteria, we obtain a non-trivial family of payment mechanisms. Moreover, we provide precise characterizations of cost critical and time critical mechanisms.

This paper reports on a mutual information (MI) based algorithm for automatic extrinsic calibration of a 3D laser scanner and optical camera system. By using MI as the registration criterion, our method is able to work in situ without the need for any specific calibration targets, which makes it practical for in-field calibration. The calibration parameters are estimated by maximizing the mutual information obtained between the sensor-measured surface intensities. We calculate the Cramer-Rao-Lower-Bound (CRLB) and show that the sample variance of the estimated parameters empirically approaches the CRLB for a sufficient number of views. Furthermore, we compare the calibration results to independent ground-truth and observe that the mean error also empirically approaches to zero as the number of views are increased. This indicates that the proposed algorithm, in the limiting case, calculates a minimum variance unbiased (MVUB) estimate of the calibration parameters. Experimental results are presented for data collected by a vehicle mounted with a 3D laser scanner and an omnidirectional camera system.

Finding a meaningful way of characterizing the difficulty of partially observable Markov decision processes (POMDPs) is a core theoretical problem in POMDP research. State-space size is often used as a proxy for POMDP difficulty, but it is a weak metric at best. Existing work has shown that the covering number for the reachable belief space, which is a set of belief points that are reachable from the initial belief point, has interesting links with the complexity of POMDP planning, theoretically. In this paper, we present empirical evidence that the covering number for the reachable belief space (or just ``covering number", for brevity) is a far better complexity measure than the state-space size for both planning and learning POMDPs on several small-scale benchmark problems. We connect the covering number to the complexity of learning POMDPs by proposing a provably convergent learning algorithm for POMDPs without reset given knowledge of the covering number.