Wikipedia provides an enormous amount of background knowledge to reason about the semantic relatedness between two entities. We propose Wikipedia-based Distributional Semantics for Entity Relatedness (DiSER), which represents the semantics of an entity by its distribution in the high dimensional concept space derived from Wikipedia. DiSER measures the semantic relatedness between two entities by quantifying the distance between the corresponding high-dimensional vectors. DiSER builds the model by taking the annotated entities only, therefore it improves over existing approaches, which do not distinguish between an entity and its surface form. We evaluate the approach on a benchmark that contains the relative entity relatedness scores for 420 entity pairs. Our approach improves the accuracy by 12% on state of the art methods for computing entity relatedness. We also show an evaluation of DiSER in the Entity Disambiguation task on a dataset of 50 sentences with highly ambiguous entity mentions. It shows an improvement of 10% in precision over the best performing methods. In order to provide the resource that can be used to find out all the related entities for a given entity, a graph is constructed, where the nodes represent Wikipedia entities and the relatedness scores are reflected by the edges. Wikipedia contains more than 4.1 millions entities, which required efficient computation of the relatedness scores between the corresponding 17 trillions of entity-pairs.

Hershman and Lieb, 1988) However, Ward et al. (Ward et al. 1999) have convincingly argued an alternative While it is relatively easy to recognize a creative deed, it is view that "[…] creative capacity is an essential property of extremely difficult (as demonstrated by creativity research normative human cognition and […] the relevant processes so far) to define what creativity is. The past (almost 70) are open to investigation". In support of this view, I would years of research definitely shed some light on different like to mention the research of Picciuto and Carruthers aspects of creativity, but we are still far from a commonly (Picciuto and Carruthers, 2012) that put forward the agreed upon definition of it and consequently a deep hypothesis that pretense play might be the key factor in understanding of this phenomenon. For an extended understanding creativity. Pretense play occurs typically in historical overview of creativity research, please refer to children at about the age of 18 months and is universal (Stojanov, 2013). Here are four branches which can be across all human cultures.

Programming a robot to perform tasks requires training that is beyond the skill level of most individuals. To address this issue, we focus on developing a method that identifies keywords used to convey task knowledge among people and a framework that uses these keywords as conditions for knowledge acquisition by the robot learner. The methodology includes generalizing task modeling and providing a robot learner the ability to learn and improve its skills through accumulated experience gained from interaction with humans. More specifically, the aim of this research addresses the issues of knowledge encoding, acquisition, and retrieval through interactive instance-based learning (IIBL). In interaction studies, the benefit of using such a robot learner is in promoting social behaviors that results from the participant taking on an active role as teacher. Our recent experiment with 33 participants, including 19 typically developing children, and a pilot study with two children with autism spectrum disorder showed that IIBL provides a framework for designing an effective robot learner, and that the robot learner successfully increases the amount of social interactions initiated by the participants.

Motivated by generating personalized recommendations using ordinal (or preference) data, we study the question of learning a mixture of MultiNomial Logit (MNL) model, a parameterized class of distributions over permutations, from partial ordinal or preference data (e.g. pair-wise comparisons). Despite its long standing importance across disciplines including social choice, operations research and revenue management, little is known about this question. In case of single MNL models (no mixture), computationally and statistically tractable learning from pair-wise comparisons is feasible. However, even learning mixture with two MNL components is infeasible in general. Given this state of affairs, we seek conditions under which it is feasible to learn the mixture model in both computationally and statistically efficient manner. We present a sufficient condition as well as an efficient algorithm for learning mixed MNL models from partial preferences/comparisons data. In particular, a mixture of $r$ MNL components over $n$ objects can be learnt using samples whose size scales polynomially in $n$ and $r$ (concretely, $r^{3.5}n^3(log n)^4$, with $r\ll n^{2/7}$ when the model parameters are sufficiently incoherent). The algorithm has two phases: first, learn the pair-wise marginals for each component using tensor decomposition; second, learn the model parameters for each component using Rank Centrality introduced by Negahban et al. In the process of proving these results, we obtain a generalization of existing analysis for tensor decomposition to a more realistic regime where only partial information about each sample is available.

Crowdsourcing is a popular paradigm for effectively collecting labels at low cost. The Dawid-Skene estimator has been widely used for inferring the true labels from the noisy labels provided by non-expert crowdsourcing workers. However, since the estimator maximizes a non-convex log-likelihood function, it is hard to theoretically justify its performance. In this paper, we propose a two-stage efficient algorithm for multi-class crowd labeling problems. The first stage uses the spectral method to obtain an initial estimate of parameters. Then the second stage refines the estimation by optimizing the objective function of the Dawid-Skene estimator via the EM algorithm. We show that our algorithm achieves the optimal convergence rate up to a logarithmic factor. We conduct extensive experiments on synthetic and real datasets. Experimental results demonstrate that the proposed algorithm is comparable to the most accurate empirical approach, while outperforming several other recently proposed methods.

Psychologists and social scientists have been researching creativity in humans for several years, and it has gained the attention of artificial intelligence and robotics researchers as well. In this abstract, we discuss the emotional and conversational interface required for a humanoid robot to socially interact with children in order to learn new creative concepts. We briefly describe the approach we are taking to develop such a humanoid robot that can collaborate with children to discover creative concepts.

The agent is "always on," i.e. it is continuously available and aware (using a camera and infrared motion sensor) when the user is in its presence and can initiate interaction with the user, rather than requiring the user login to begin interaction. We expect that the agent will help reduce the user's isolation not just by always being around but also by specific activities that connect the user with friends, family and the local community. Our goal is for the agent to be a natural, humanlike presence that "resides" in the user's apartment. Beginning in the late summer of 2014, we will be placing our agents with users for a monthlong evaluation study. Figure 1: Virtual agent interface -- "Karen" Three issues of our project directly concern the topics of this workshop are: (1) the embodiment of the agent, (2) the engagement behaviors that are associated with being "always measures we will be using are questionnaires that assess the on," and (3) AI tools for support intelligent behavior.

We give an overview of how the CADENCE architecture addresses the problem of turn-taking in embodied interaction.

For people constrained to picture based communication, the expression of interest in a question answering (QA) or information retrieval (IR)scenario is highly limited. Traditionally, alternative and augmentative communication (AAC) methods (such as gestures and communication boards) are utilised. But only few systems allow users to produce whole utterances or sentences that consist of multiple words; work to generate them automatically is a promising direction in the big data context.In this paper, we provide a dedicated access method for the open-domain QA and IR context. We propose a method for the user to search for additional symbols to be added to the communication board in real-time while using access to big data sources and context based filtering when the desired symbol is missing. The user can select a symbol that is associated with the desired concept and the system searches for images on the Internet - here, in Wikipedia - with the purpose of retrieving an appropriate symbol or picture. Querying for candidates is performed by estimating semantic relatedness between text fragments using explicit semantic analysis (ESA).

In this paper a method to integrate inference and machine learning is proposed. Execution of learning algorithm is defined as a complex inference rule, which generates intrinsically new knowledge. Such a solution makes the reasoning process more creative and allows to re-conceptualize agent's experiences depending on the context. Knowledge representation used in the model is based on the Logic of Plausible Reasoning (LPR). Three groups of knowledge transmutations are defined: search transmutations that are looking for the information in data, inference transmutations that are formalized as LPR proof rules, and complex ones that can use machine learning algorithms or knowledge representation change operators. All groups can be used by inference engine in a similar manner. In the paper appropriate system model and inference algorithm are proposed. Additionally, preliminary experimental results are presented.