This paper outlines a study that investigates spatial language for use in human-robot communication. The scenario studied is a home setting in which the elderly resident has misplaced an object, such as eyeglasses, and the robot will help the resident find the object. We present results from phase I of the study in which we investigate spatial language generated to a human addressee or a robot addressee in a virtual environment and highlight differences between younger and older adults. Drawn from these results, a discussion is included of needed robot capabilities, such as an approach that addresses varying perspectives used and recognition of furniture items for use as spatial references.

Over a series of evaluation experiments conducted using naive judges recruited and managed via Amazon's Mechanical Turk facility using a task from information retrieval (IR), we show that a SVM shows itself to have a very high accuracy when the machine-learner is trained and tested on a single task and that the method was portable from more complex tasks to simpler tasks, but not vice versa.

Inverse reinforcement learning (IRL) techniques (Ng and Russell, 2000) provide a foundation for detecting abnormal agent behavior and predicting agent intent through estimating its reward function. Unfortunately, IRL algorithms suffer from the large dimensionality of the reward function space. Meanwhile, most applications that can benefit from an IRL-based approach to assessing agent intent, involve interaction with an analyst or domain expert. This paper proposes a procedure for scaling up IRL by eliciting good IRL basis functions from the domain expert. Further, we propose a new paradigm for modeling limited rationality. Unlike traditional models of limited rationality that assume an agent making stochastic choices with the value function being treated as if it is known, we propose that observed irrational behavior is actually due to uncertainty about the cost of future actions. This treatment normally leads to a POMDP formulation which is unnecessarily complicated, and we show that adding a simple noise term to the value function approximation accomplishes the same at a much smaller cost.

Motivated by efficient algorithms for solving combina- torial and discrete instances of the multi-agent pathfinding problem, this report investigates ways to utilize such solutions to solve similar problems in the continuous domain. While a simple discretization of the space which allows the direct application of combinatorial algorithms seems like a straightforward solution, there are additional constraints that such a discretization needs to satisfy in order to be able to provide some form of completeness guarantees in general configuration spaces. This report reviews ideas on how to utilize combinatorial algorithms to solve continuous multi-agent pathfinding problems. It aims to collect feedback from the community regarding the importance and the complexity of this challenge, as well as the appropriateness of the solutions considered here.

In crowdsourced relevance judging, each crowd workertypically judges only a small number of examples,yielding a sparse and imbalanced set of judgments inwhich relatively few workers influence output consensuslabels, particularly with simple consensus methodslike majority voting. We show how probabilistic matrixfactorization, a standard approach in collaborative filtering,can be used to infer missing worker judgments suchthat all workers influence output labels. Given completeworker judgments inferred by PMF, we evaluate impactin unsupervised and supervised scenarios. In thesupervised case, we consider both weighted voting andworker selection strategies based on worker accuracy.Experiments on a synthetic data set and a real turk dataset with crowd judgments from the 2010 TREC RelevanceFeedback Track show promise of the PMF approachmerits further investigation and analysis.

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.

We have explored with three notions: conceptualization, and contextualization from situated cognition, and psychic reflection from activity theory for identifying activities into a method called the activity states framework (ASF). The purpose of our work is to build an AI system based on ASF for the identification of collaborators activities during situated context, e.g., collaborators are engaged in a tutorial activity. In this paper, we will introduce and propose how Web-mediated collaborative activities can be identified from collaborators communication exchanges by applying the ASF.

The importance of URLs in the representation of a document cannot be overstated. Shorthand mnemonics such as ``wiki'' or ``blog'' are often embedded in a URL to convey its functional purpose or genre. Other mnemonics have evolved from use (e.g., a Wordpress particle is strongly suggestive of blogs). Can we leverage from this predictive power to induce the genre of a document from the representation of a URL? This paper presents a methodology for webpage genre classification from URLs which, to our knowledge, has not been previously attempted. Experiments using machine learning techniques to evaluate this claim show promising results and a novel algorithm for character n-gram decomposition is provided. Such a capability could be useful to improve personalized search results, disambiguate content, efficiently crawl the Web in search of relevant documents, and construct behavioral profiles from clickstream data without parsing the entire document.

QuerioCity aims at creating an ecosystem for managing and accessing the information of a city, with a particular focus on transforming, integrating and querying heterogenous semistructured data in an open environment. This raises unique challenges in terms of: - Fitness-for-use. The users of the system are not data integration experts and not qualified to use industry data integration tools. Furthermore, they are not able to query data using structured query languages. The domain of the information is very broad and open.

In the field of probabilistic robotics, a central problem is to determine a robot's state given knowledge of a time series of control commands and sensor readings. The effects of control commands and the behavior of sensor devices are both modeled probabilistically. A variety of methods are available for deriving the robot's belief state, which is a probabilistic representation of the robot's true state (which cannot be directly known). This paper presents a series of five assignments to teach this material at the advanced undergraduate/graduate level. The theoretical aspect of the work is reinforced by practical implementation exercises using ROS (Robot Operating System), and the Bilibot, an educational robot platform.