The group lasso is a penalized regression method, used in regression problems where the covariates are partitioned into groups to promote sparsity at the group level. Existing methods for finding the group lasso estimator either use gradient projection methods to update the entire coefficient vector simultaneously at each step, or update one group of coefficients at a time using an inexact line search to approximate the optimal value for the group of coefficients when all other groups' coefficients are fixed. We present a new method of computation for the group lasso in the linear regression case, the Single Line Search (SLS) algorithm, which operates by computing the exact optimal value for each group (when all other coefficients are fixed) with one univariate line search. We perform simulations demonstrating that the SLS algorithm is often more efficient than existing computational methods. We also extend the SLS algorithm to the sparse group lasso problem via the Signed Single Line Search (SSLS) algorithm, and give theoretical results to support both algorithms.

The Bradley-Terry model is a popular approach to describe probabilities of the possible outcomes when elements of a set are repeatedly compared with one another in pairs. It has found many applications including animal behaviour, chess ranking and multiclass classification. Numerous extensions of the basic model have also been proposed in the literature including models with ties, multiple comparisons, group comparisons and random graphs. From a computational point of view, Hunter (2004) has proposed efficient iterative MM (minorization-maximization) algorithms to perform maximum likelihood estimation for these generalized Bradley-Terry models whereas Bayesian inference is typically performed using MCMC (Markov chain Monte Carlo) algorithms based on tailored Metropolis-Hastings (M-H) proposals. We show here that these MM\ algorithms can be reinterpreted as special instances of Expectation-Maximization (EM) algorithms associated to suitable sets of latent variables and propose some original extensions. These latent variables allow us to derive simple Gibbs samplers for Bayesian inference. We demonstrate experimentally the efficiency of these algorithms on a variety of applications.

In this paper, we explore ideas regarding a formal logical model which allows for the use of stories to persuade autonomous software agents to take a particular course of action. This model will show how typical stories – sequences of events that form a meaningful whole – can be used to set an example for an agent and how the agent might adapt his own values and choices according to the values and choices made by the characters in the story.

Lehnert's Plot Units (Lehnert 1981) or Rumelhart's Story Grammars (Rumelhart 1980), and naturally, one would like We give semiformal We aim at capturing the informal human notion of equivalence definitions in § 2 and then give a few examples (without any of stories in a formal system in such a way that formal details) in § 3. two stories are perceived as equivalent when their formal representations are isomorphic (cf. There is no unique "human Comparing the adequacy of frameworks is not a formal task, notion of equivalence of stories" as the research on analogical but deals with the degree of representation of the informal reasoning shows (Rattermann and Gentner 1987; notions in the formal setting.

The AAAI Fall Symposium on Meta-Cognitive Educational - What are the theoretical foundations and how are they articulated Systems: One Step Forward is the second edition of the successful in CBLEs? MCES implemented as CBLEs are designed to interact with - What are the main aspects of metacognition, selfregulation users, and support their learning and decision-making processes. Can MCES actually foster they need to plan their learning activities, to adapt their learners to be self-regulating agents? How can a MCES learning strategies to meet learning goals, become aware of be autonomous and increase its knowledge to match the changing task conditions, and the dynamic aspects of the learners evolving skills and knowledge? MCES may not be embodied, prior to, during, and after they have been involved in but does it help if they act as intentional agents? the learning environment.

In the literature on reasoning on the basis of evidence, two traditions exist: one argument-based, and one based on narratives. Recently, we have proposed a hybrid perspective in which argumentation and narratives are combined. This formalized hybrid theory has been tested in a sense-making software prototype for criminal investigators and decision makers. In the present paper, we elaborate on the role of commonsense knowledge. We argue that two kinds of knowledge are essential: argumentation schemes and story schemes. We discuss some of the research issues that need to be addressed.

This paper aims at bringing social robots closer to naive users. A Natural Programming System that allows the end-user to give instructions to a Social Robot has been developed. The instructions derive in a sequence of actions and conditions, that can be executed while the own sequence verbal edition continues. A Dialogue Manager System (DMS) has been developed in a Social Robot. The dialog is described in a voiceXML structure, where a set of information slots is defined. These slots are related to the necessary attributes for the construction of the sequence in execution time. The robot can make specific requests on encountering unfilled slots. Temporal aspects of dialog such as barge-in property, mixed-initiative, or speech intonation control are also considered. Dialog flow is based on Dialog Acts. The dialog specification has also been extended for multimodality management. The presented DMS has been used as a part of a Natural Programming System but can also be used for other multimodal humanrobot interactive skills.

This paper presents a robust methodology for grounding vocabulary in robots. A social language grounding experiment is designed, where, a human instructor teaches a robotic agent the names of the objects present in a visually shared environment. Any system for grounding vocabulary has to incorporate the properties of gradual evolution and lifelong learning. The learning model of the robot is adopted from an ongoing work on developing systems that conform to these properties. Significant modifications have been introduced to the adopted model, especially to handle words with multiple meanings. A novel classification strategy has been developed for improving the performance of each classifier for each learned category. A set of six new nearest-neighbor based classifiers have also been integrated into the agent architecture. A series of experiments were conducted to test the performance of the new model on vocabulary acquisition. The robot was shown to be robust at acquiring vocabulary and has the potential to learn a far greater number of words (with either single or multiple meanings).

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).

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.