Websites that encourage consumers to research, rate, and review products online have become an increasingly important factor in purchase decisions. This increased importance has been accompanied by a growth in deceptive opinion spam - fraudulent reviews written with the intent to sound authentic and mislead consumers. In this study, we pool deceptive reviews solicited through crowdsourcing with actual reviews obtained from product review websites. We then explore several human- and machine-based assessment methods to spot deceptive opinion spam in our pooled review set. We find that the combination of human-based assessment methods with easily-obtained statistical information generated from the review text outperforms detection methods using human assessors alone.

Such systems humans are much more efficient at abstracting and matching take advantage of human abilities--particularly in vision, visual cues across piece borders based on their content. For natural language, and pattern recognition--to handle example, a person looking at a piece of a shredded document instances and aspects of problems that are difficult for can recognize a letter that is only partially present, and an computers. The ESP game (von Ahn and Dabbish 2008), experienced archaeologist looking at a particular piece of FoldIt (Cooper et al. 2010), and reCAPTCHA (von Ahn et a broken artifact can recognize unique patterns that extend al. 2008) are a few examples of successful systems that draw beyond the fragment. Unfortunately, for a human to find a on human contributors and machine computations to tackle matching piece still requires scanning through the pieces, problems in image labeling, protein folding, and text digitization.

Crowdsourcing utilizes human ability by distributing tasks to a large number of workers. It is especially suitable for solving data clustering problems because it provides a way to obtain a similarity measure between objects based on manual annotations, which capture the human perception of similarity among objects.This is in contrast to most clustering algorithms that face the challenge of finding an appropriate similarity measure for the given dataset. Several algorithms have been developed for crowdclustering that combine partial clustering results, each obtained by annotations provided by a different worker, into a single data partition. However, existing crowd-clustering approaches require a large number of annotations, due to the noisy nature of human annotations, leading to a high computational cost in addition to the large cost associated with annotation. We address this problem by developing a novel approach for crowclustering that exploits the technique of matrix completion. Instead of using all the annotations, the proposed algorithm constructs a partially observed similarity matrix based on a subset of pairwise annotation labels that are agreed upon by most annotators. It then deploys the matrix completion algorithm to complete the similarity matrix and obtains the final data partition by applying a spectral clustering algorithm to the completed similarity matrix. We show, both theoretically and empirically, that the proposed approach needs only a small number of manual annotations to obtain an accurate data partition. In effect, we highlight the trade-off between a large number of noisy crowdsourced labels and a small number of high quality labels.

With the advent of the increasingly participatory Internet and the growing power of the crowd, “Serious Games” have proven to be a fertile approach for gathering task-specific natural language data at very low cost. In this paper we outline a game we call Doodling, based on the sketch-and-convey metaphor used in the popular board game Pictionary(R), with the goal of generating useful natural language data. We explore whether such a paradigm can be successfully extended for conveying more complex syntactic and semantic constructs than the words or short phrases typically used in the board game. Through a series of user experiments, we show that this is indeed the case, and that valuable parallel language data may be produced as a byproduct. In addition, we explore extensions to this paradigm along two axes – going online (vs. face-to-face) and going cross-lingual. The results in each of the sets of experiments confirm the potential of Doodling game to generate data in large quantities and across languages, and thus provide a new means of developing data sets and technologies for resource-poor languages.

Online labor marketplaces offer the potential to automate a variety of tasks too difficult for computers, but present requesters with significant difficulties in obtaining accurate results. We share experiences from building MobileWorks, a crowd platform that departs from the marketplace model to provide robust, high-quality results. Three architectural contributions yield measurably improved accuracy on input tasks.  A dynamic work routing system identifies expertise in the crowd and ensures that all work posted into the system is completed with bounded completion times and at fair worker prices. A peer management system ensures that incorrect answers are prevented by experienced members of the crowd. Last, social interaction techniques give the best workers the ability and incentives to manage, teach & supervise other members of the crowd, as well as to clarify tasks. This process filters worker error and allows the crowd to collectively learn how to solve unfamiliar tasks. (extended abstract)

A general consensus on representation of emotions and feelings in cognitive architectures is currently missing; yet artificial emotional intelligence is vital for the integration of future robots into the human society. This work introduces one possible approach to representation and processing of emotional mental states and attitudes, that allows for implementation of control of agent behavior by emotions as well as for recognition of emotional motivations in another agent's behavior. One particular advantage of this approach is that it allows for representation and processing of complex/social emotional attitudes, like shame, jealousy, resentment, or humor. The proposed validation of the approach is based on simulation of the emergence of emotional relationships in a small group of agents in a virtual environment.

A robot coexisting with humans must not only be able to successfully perform physical tasks, but must also be able to interact with humans in a socially appropriate manner. In many social settings, this involves the use of social signals like gaze, facial expression, and language. In this paper we discuss preliminary work focusing on the problem of combining social interaction with task-based action in a dynamic, multiagent bartending domain, using an embodied robot. We discuss how social states are inferred from low-level sensors, using vision and speech as input modalities, and present a planning approach that models task, dialogue, and social actions in a simple bartending scenario. This approach allows us to build interesting plans, which have been evaluated in a real-world study with human subjects, using a general purpose, off-the-shelf planner, as an alternative to more mainstream methods of interaction management.

The most critical moves that determine the success of a manipulation task are performed within the close vicinities of the object prior to grasping, and the goal prior to the final placement. Memorizing these state-action sequences and reusing them can dramatically improve the task efficiency, whereas even the state-of-the-art planning algorithms may require significant amount of time and computational resources to generate a solution from scratch depending on the complexity and the constraints of the task. In this paper, we propose a hybrid approach that combines the reliability of the past experiences gained through demonstration and the flexibility of a generative motion planning algorithm, namely RRT*, to improve task execution efficiency. As a side benefit of reusing these final moves, we can dramatically reduce the number of nodes used by the generative planner, hence the planning time, by either early-terminating the planner when the generated plan reaches a "recalled state", or deliberately biasing it towards the memorized state-action sequences that are feasible at the moment. This complementary combination of the already available partial plans and the generated ones yield to fast, reliable, and repeatable solutions.

Organizing committee for the  Cognitive Robotics Workshop held at AAAI-12.

The problem of designing and building effective assistive systems for human agents performing professional knowledge-intensive activities, or knowledge-works is of great interest and has wide implications. In this paper we propose a new approach for solving the problem. The approach is based on activity-context aware computation paradigm that can lead to flexible yet robust systems for holistic support in performing complex knowledge-works. To this end, we also outline here the notion of activity-context and the idea of activity-models as core artifacts used by such systems embodying the notion.