Today millions of web-users express their opinions about many topics through blogs, wikis, fora, chats and social networks. For sectors such as e-commerce and e-tourism, it is very useful to automatically analyze the huge amount of social information available on the Web, but the extremely unstructured nature of these contents makes it a difficult task. SenticNet is a publicly available resource for opinion mining built exploiting AI and Semantic Web techniques. It uses dimensionality reduction to infer the polarity of common sense concepts and hence provide a public resource for mining opinions from natural language text at a semantic, rather than just syntactic, level.

This position paper presents an approach to computational narrative based in cognitive linguistics and sociolinguistics accounts of conceptual blending, metaphor, and narrative, multimedia semantics, human-centered interface design, and digital media art practice. In particular, as a case study, we describe the Living Liberia Fabric, an AI-based interactive narrative system developed in affiliation with the Truth and Reconciliation Commission (TRC) of Liberia to memorialize a fourteen-year civil war. The Living Liberia Fabric project is led by Fox Harrell and executed in the Imagination, Computation, and Expression (ICE) Laboratory at Georgia Tech. The system exemplifies a cultural computing approach (grounding computing practices in a wider range of specific cultural traditions and values than those that are privileged in computer science).

We propose a framework for a robot to participate in and activate multiparty conversation. In multiparty conversation, the robot should select its behavior based on both linguistic information and participation structure. In this paper, we focus on multiparty conversation game "Nandoku," which is often played in elderly care facilities. The robot acts as one of the participants, and tries to promote the communication activeness. The framework handles the dialogue situation from three aspects: multiparty conversation, game progress and communication activation, and selects the most effective robot's behavior according to these three aspects.

As a precursor to learning to use language an infant has to acquire preliminary linguistic skills, including the ability to recognize and produce word forms without meaning. This develops out of babbling, through vocal interaction with carers. We report on evidence from developmental psychology and from neuroscientific research that supports a dual process approach to language learning. We describe a simulation of the transition from babbling to the recognition of first word forms in a simulated robot interacting with a human teacher. This precedes interactions with the real iCub robot.

The analysis of career paths suffers from a lack of exploratory tools and dynamic models, due in part to the inherent high dimensionality of the problem. Paths may be understood as directed traversals through a graph whose nodes consist of "job types," which we define as industry and occupation pairs. We want to develop tools to understand and detect high-level features of  both the labor market and the workers moving through it — career dynamics. To do this, we map the discrete space of jobs into a d-dimensional continuous space; proximity between jobs will mean that they are "close" to each other in a non-negligible subset of career paths. This embedding allows one to visualize the job landscape.  Moreover, we can map individual or groups of career paths to this space, extract features of their collective structure, and construct statistical tests comparing groups by means of this mapping.

We present an approach for acquiring common sense knowledge from social interaction. We argue that social common sense should be learned from daily interactions using implicit user's feedbacks and requires shared understanding of social situations. A service-oriented architecture, inspired from cognitive science, that foster mutual understanding between a smart environment and its inhabitants is presented. The method makes use of ConceptNet to work with common sense knowledge. We are able to successfully use and learn common sense knowledge.

Learning to plausibly reason with minimal user intervention could significantly improve knowledge acquisition. We describe how to integrate graph-based heuristic generalization, higher-order knowledge, and reinforcement learning to learn to produce plausible inferences with only small amounts of user training. Experiments on ResearchCyc KB contents show significant improvement in Q/A performance with high accuracy.

In this paper, we propose a novel human-robot-interaction framework for the purpose of rapid visual scene understanding. The task of the robot is to correctly enumerate how many separate objects there are in the scene and to describe them in terms of their attributes. Our approach builds on top of a state-of-the-art 3D segmentation method segmenting stereo reconstructed point clouds into object hypotheses and combines it with a natural dialog system. By putting a `human in the loop', the robot gains knowledge about ambiguous situations beyond its own resolution. Specifically, we are introducing an entropy-based system to spot the poorest object hypotheses and query the user for arbitration. Based on the information obtained from the human-to-robot dialog, the scene segmentation can be re-seeded and thereby improved. We present experimental results on real data that show an improved segmentation performance compared to segmentation without interaction.

Games With A Purpose (or GWAP) provide an interesting way to collect data from web users. With over a million sentences collected and growing steadily, data verification becomes increasingly important. This research explores the alternative of designing human computation games specifically for verification purposes. Two games, Top10 and Pirate and Ghost, are designed for commonsense data verification. Top10 is a single-player game, in which the player attempts to guess the top answers to a given question. We use the frequency data to verify if the assertion is truly common. Pirate and Ghost is a multiplayer guessing role playing game in a network of concepts from the CSKB. We use the game data to identify the relation between two concepts. This paper presents the design of both games, and evaluate the efficiency and precision of each with two experiments. The results show that the two games can be coupled to achiever higher efficiency and precision in the data verification process.

In order to better understand the mechanisms that lead to resiliency in natural systems, to support decisions that lead to greater resiliency in systems we effect, and to create models that will utilized in highly resilient systems, methods for resiliency analysis will be required. Existing methods and technology for robustness analysis provide a foundation for a rigorous approach to resiliency analysis, but extensions are necessary to address the multiple time scales that must be modeled to understand highly adaptive systems. Further, if resiliency modeling is to be effective, it must be contextualized, requiring that the supporting software will need to mirror the systems being modeling by being pace layered and adaptive.