The neural basis of speech recognition and, more generally, sound processing is not well understood. A simple subset of the task of speech recognition, learning to categorise vowel sounds, provides some insights into the more general problems. A simulated neural system that performs this task is described. The system is based on relatively accurate fatiguing leaky integrate and fire neurons, and learns to categorise three categories of vowel sounds. The input to the system is in the form of neural stimulation that relatively accurately reflects the response of biological neurons in the ear to auditory input. The system correctly categorises 91.71% of the vowel sounds using a five-fold test. The system is a sound model of the neuropsychological task of phoneme categorisation, all be it a far from perfect model. As such, it provides an entry into a better understanding of the neuro-psychological mechanisms behind sound processing.

The DARPA Mind’s Eye program seeks to develop in machines a capability that currently exists only in animals: visual intelligence. This paper describes a Neural-Symbolic Cognitive Agent that integrates neural learning, symbolic knowledge representation and temporal reasoning in a visual intelligent system that can reason about actions of entities observed in video. Results have shown that the system is able to learn and represent the underlying semantics of the actions from observation and use this for several visual intelligent tasks, like recognition, description, anomaly detection and gap-filling.

In our ongoing work, we aim to control a team of agents soas to achieve a prescribed goal state while being confidentthat collisions with other agents are avoided. Each agent isassociated with a feedback controlled plant, whose continu-ous state trajectories follow some stochastic differential dy-namics. To this end we describe a collision-detection modulebased on a distribution-independent probabilistic bound andemploy a fixed priority method to resolve collisions. Dueto their practical importance, multi-agent collision avoid-ance and control have been extensively studied across differ-ent communities including AI, robotics and control. How-ever, these works typically assume linear and discrete dy-namic models; by contrast, our work intends to overcomethese limitations and to present solutions for continuousstate space. While our current experiments were conductedwith linear stochastic differential equation (SDE) modelswith state-independent noise (yielding Gaussian processes)we believe that our approach could also be applicable to non-Gaussian cases with state-dependent uncertainties.

Computational systems can use sociocultural knowledge to understand human behavior and interact with humans in more natural ways. However, such systems are limited by their reliance on hand-authored sociocultural knowledge and models. We introduce an approach to automatically learn robust, script-like sociocultural knowledge from crowdsourced narratives. Crowdsourcing, the use of anonymous human workers, provides an opportunity for rapidly acquir­ing a corpus of examples of situations that are highly specialized for our purpose yet sufficiently varied, from which we can learn a versatile script. We describe a semi-automated process by which we query human workers to write natural language narrative examples of a given situation and learn the set of events that can occur and the typical even ordering.

Crowdsourcing markets, such as Amazon Mechanical Turk, provide a platform for matching prospective workers around the world with tasks. However, they are often plagued by workers who attempt to exert as little effort as possible, and requesters who deny workers payment for their labor. For crowdsourcing markets to succeed, it is essential to discourage such behavior. With this in mind, we propose a framework for the design and analysis of incentive mechanisms based on social norms, which consist of a set of rules that participants are expected to follow, and a mechanism for updating participants’ public reputations based on whether or not they do. We start by considering the most basic version of our model, which contains only homogeneous participants and randomly matches workers with tasks. The optimal social norm in this setting turns out to be a simple, easily comprehensible incentive mechanism in which market participants are encouraged to play a tit-for-tat-like strategy. This simple mechanism is optimal even when the set of market participants changes dynamically over time, or when some fraction of the participants may be irrational. In addition to the basic model, we demonstrate how this framework can be applied to situations in which there are heterogeneous users by giving several illustrating examples. This work is a first step towards a complete theory of incentive design for crowdsourcing systems. We hope to build upon this framework and explore more interesting and practical aspects of real online labor markets in our future work.

With the proliferation of online labor markets and other social computing platforms, online experiments have become a low-cost and scalable way to empirically test hypotheses and mechanisms in both human computation and social science. Yet, despite the potential in designing more powerful and expressive online experiments using multiple subjects, researchers still face many technical and logistical difficulties. We see synchronous and longitudinal experiments involving real-time interaction between participants as a dual-use paradigm for both human computation and social science, and present TurkServer, a platform that facilitates these types of experiments on Amazon Mechanical Turk. Our work has the potential to make more fruitful online experiments accessible to researchers in many different fields.

Contextual knowledge is essential in answering questions given specific observations. While recent approaches to building commonsense knowledge basesvia text mining and/or crowdsourcing are successful,contextual knowledge is largely missing. To addressthis gap, this paper presents SocialExplain, a novel approach to acquiring contextual commonsense knowledge from explanations of social content. The acquisition process is broken into two cognitively simple tasks:to identify contextual clues from the given social content, and to explain the content with the clues. An experiment was conducted to show that multiple piecesof contextual commonsense knowledge can be identi-fied from a small number of tweets. Online users verified that 92.45% of the acquired sentences are good,and 95.92% are new sentences compared with existingcrowd-sourced commonsense knowledge bases.

We report results from a human computation study that tests the extent to which output agreement games are better than traditional methods in terms of increasing quality of labels and motivation of voluntary workers on a task with a gold standard. We built an output agreement game that let workers recruited from Amazon's Mechanical Turks label the semantic textual similarity of 20 sentence pairs. To compare and test the effects of the major components of the game, we created interfaces that had different combinations of a gaming environment (G), social interaction (S), and feedback (F). Our results show that the main reason that an output agreement game can collect more high-quality labels is the gaming environment (scoring system, leaderboard, etc). On the other hand, a worker is much more motivated to voluntarily do the task if he or she can do it with another worker (i.e., with social interaction). Our analysis provides human computation researchers important insight on understanding how and why the method of Game with a Purpose (GWAP) can generate high-quality outcomes and motivate more voluntary workers.

Human computation is a relatively new research area that studies how to build intelligent systems that involve human computers, with each of them performing computation (for example, image classification, translation, and protein folding) that leverages human intelligence, but challenges even the most sophisticated AI algorithms that exist today. With the immense growth of the Web, human computation systems can now leverage the abilities of an unprecedented number of Internet users to perform complex computation. Various genres of human computation applications are available today, including games with a purpose (for example, the ESP Game) that generate useful data through gameplay, crowdsourcing marketplaces (for example, Amazon Mechanical Turk) that coordinate workers to perform tasks for monetary rewards, and identity verification systems (for example, reCAPTCHA) that generate useful data through users performing computation for access to online content. Despite the variety of human computation applications, there exist many common core research issues. How can we design mechanisms for querying human computers in such a way that incentivizes or encourages truthful responses?

Linguistic communication relies on non-linguistic context toconvey meaning. That context might include, for instance, recent orlong-term experience, semantic knowledge of the world, or objects and events in the immediate environment. In this paper, we describe embodied agents instantiated in Soar cognitive architecture that use context derived from their linguistic, perceptual, procedural and semantic knowledge for comprehending imperative sentences.