This paper reports on the linguistic analysis of a tag set of nearly 50,000 tags collected as part of the steve.museum project. The tags describe images of objects in museum collections. We present our results on morphological, part of speech and semantic analysis. We demonstrate that deeper tag processing provides valuable information for organizing and categorizing social tags. This promises to improve access to museum objects by leveraging the characteristics of tags and the relationships between them rather than treating them as individual items. The paper shows the value of using deep computational linguistic techniques in interdisciplinary projects on tagging over images of objects in museums and libraries. We compare our data and analysis to Flickr and other image tagging projects.

Partially observable stochastic games (POSGs) are an attractive model for many multi-agent domains, but are computationally extremely difficult to solve. We present a new model, Markov games of incomplete information (MGII) which imposes a mild restriction on POSGs while overcoming their primary computational bottleneck. Finally we show how to convert a MGII into a continuous but bounded fully observable stochastic game. MGIIs represents the most general tractable model for multi-agent reinforcement learning to date.

Frequently, it is advantageous for an agent to model other agents in order to predict their behavior during an interaction. Modeling others as rational has a long tradition in AI and game theory, but modeling other agents’ departures from rationality is difficult and controversial. This paper proposes that bounded rationality be modeled as errors the agent being modeled is making while deciding on its action. We are motivated by the work on quantal response equilibria in behavioral game theory which uses Nash equilibria as the solution concept. In contrast, we use decision-theoretic maximization of expected utility. Quantal response assumes that a decision maker is rational, i.e., is maximizing his expected utility, but only approximately so, with an error rate characterized by a single error parameter. Another agent’s error rate may be unknown and needs to be estimated during an interaction. We show that the error rate of the quantal response can be estimated using Bayesian update of a suitable conjugate prior, and that it has a finitely dimensional sufficient statistic under strong simplifying assumptions. However, if the simplifying assumptions are relaxed, the quantal response does not admit a finite sufficient statistic and a more complex update is needed. This confirms the difficulty of using simple models of bounded rationality in general settings.

Having discovered a decision principle for a well-known prima facie duty theory in biomedical ethics to resolve particular cases of a common type of ethical dilemma, we developed three applications: a medical ethics advisor system, a medication reminder system and an instantiation of this system in a Nao robot. We are now developing a general, automated method for generating from scratch the ethics needed for a machine to function in a particular domain, without making the assumptions used in our prototype systems.

Mutual exclusions provide useful information for learn- ing classes of concepts. We designed MuSweeper as a MineSweeper-like game to collect mutual exclusions from web users. Using the mechanism of an exten- sive game with Imperfect information, our experiments showed MuSweeper to collect mutual exclusions with high precision and efficiency.

We present techniques for gathering data that expose errors of automatic predictive models. In certain common settings, traditional methods for evaluating predictive models tend to miss rare-but-important errors---most importantly, rare cases for which the model is confident of its prediction (but wrong). In this paper we present a system that, in a game-like setting, asks humans to identify cases what will cause the predictive-model-based system to fail. Such techniques are valuable in discovering problematic cases that do not reveal themselves during the normal operation of the system, and may include cases that are rare but catastrophic. We describe the design of the system, including design iterations that did not quite work. In particular, the system incentivizes humans to provide examples that are difficult for the model to handle, by providing a reward proportional to the magnitude of the predictive model's error. The humans are asked to ``\emph{Beat the Machine}'' and find cases where the automatic model (``\emph{the Machine}'') is wrong. Experiments show that the humans using Beat the Machine identify more errors than traditional techniques for discovering errors in from predictive models, and indeed, they identify many more errors where the machine is confident it is correct. Further, the cases the humans identify seem to be not simply outliers, butcoherent areas missed completely by the model. Beat the machine identifies the ``unknown unknowns.''

The strategies seem designed to mitigate guilt over the parents' role in their The GenIE Assistant is an implemented proof-of-concept child's inheritance of a genetic condition. The names used computational model of normative biomedical argument to refer to the strategies in this paper and examples of generation informed by study of a corpus of letters each are listed below. All four apply to cases of written by genetic counselors to their clients (Green et al. autosomal recessive inheritance, while only the first two 2011). The goal of the model is to generate transparent apply to cases of autosomal dominant inheritance.

This paper describes the development of a corpus of blog posts that are annotated for the presence of attempts to persuade and corresponding tactics employed in persuasive messages. We investigate the feasibility of classifying blog posts as persuasive or non-persuasive on the basis of lexical features in the text and the tactics (as provided by human annotators). Annotated tactics provide substantial assistance in classifying persuasion, particularly tactics indicating formal reasoning, deontic obligation, and discussions of possible outcomes, suggesting that learning to identify tactics may be an excellent first step to detecting attempts to persuade.

The task of coordinating hundreds of mobile robots in one of Kiva System's warehouses presents many challenging multi-agent resource allocation problems. The resources include things like inventory, open orders, small shelving units, and the robots themselves. The types of resources can be classified by whether they are consumable, recycled, or scheduled. Further, the global optimization problem can be broken down into more manageable sub-problems, some of which map to (hard) versions of well known computational problems, but with a dynamic, temporal twist.

Mainly, we need kinds of virtualization technologies to offer on-demand to identify what parts of the model are really important and computing resources. There is widespread consensus that what other parts are secondary. Then, we would like to study the Future Internet will be heavily based on some kind of the scalability of the current models and identify the current successful Cloud technology. However, to master the deployment limits. Therefore, we propose to consider all existing of Cloud-based infrastructures, some hard scientific CP models in order to answer to these questions.