Different studies provide evidence that the computational psycholinguistic algorithm called Latent Semantic Analysis (LSA) allows measuring local and global coherence in texts similarly to human evaluation (Foltz, Kintsch, Landauer 1998; McNamara, Cai & Louwerse 2007; McCarthy, Briner, Rus, & McNamara, 2007; McNamara, Louwerse & Jeuniaux 2009; Louwerse, McCarthy & Graesser 2010). The texts used in all these studies are written in English and correspond to scientific and literary texts. In Spanish, there are some studies using LSA that measure the semantic similarity between texts in automatic summary assessment (Pérez, Alfonseca, Rodríguez, Gliozzo, Strapparava & Magnini 2005; León, Olmos, Escudero, Cañas & Salmerón 2006; Venegas 2007, 2009, 2011); however, automatic measurement of coherence in Spanish has not yet been sufficiently investigated. The present study aimed at identifying a global and local coherence profile in a corpus of speeches in Spanish of three Latin American Heads-of-States (Perón, Castro and Pinochet), using Latent Semantic Analysis. Local coherence is calculated through the measurement of implicit semantic similarity between adjacent sentences and global coherence through the measurement of the similarity among the semantic content of the paragraphs. The corpus under analysis corresponds to a sample of 107 speeches. The semantic space was built using a multi-register corpus and it is available through the “Interface for the measurement of lexical-semantic similarity” in the El Grial interface (www.elgrial.cl). Results showed a systematic difference between the speeches of the Heads-of-State in terms of both local and global coherence. The Bonferroni analysis established an effect that distinguishes Perón’s speeches from Pinochet’s and Castro’s speeches. This results show that Perón’s speeches are more topically related than the other leaders’, probably due to a discourse strategy to persuade voters. The identification of a profile of coherence might be relevant to predict cues of government discourse styles.

While similarity and retrieval in case-based reasoning (CBR) have received a lot of attention in the literature, other aspects of CBR, such as case reuse are less understood. Specifically, we focus on one of such, less understood, problems: "knowledge transfer". The issue we intend to elucidate can be expressed as follows: what knowledge present in a source case is transferred to a target problem in case-based inference? This paper presents a preliminary formal model of knowledge transfer and relates it to the classical notion of analogy.

Current approaches for word-sense disambiguation (WSD) try to relate the senses of the target words by optimizing a score for each sense in the context of all other words' senses. However, by scoring each sense separately, they often fail to optimize the relations between the resulting senses. We address this problem by proposing a HITS-inspired method that attempts to optimize the score for the entire sense combination rather than one-word-at-a-time. We also exploit word-sense disambiguation via topic-models, when retrieving senses from heterogeneous sense inventories. Although this entails the relaxation of several assumptions behind current WSD algorithms, we show that our proposed method E-WSD achieves better results than current state-of-the-art approaches, without the need for additional background knowledge.

Event Management is one of the most lucrative and growing professions today. At present event management is done by humans. With the growing demand for managing large events, there is a rising demand for building intelligent systems to manage events. The so called event management systems today are only data processing systems that are unable to carry out decision making task on their own. Event management systems today do not consider emergencies and risk assessment as part of their execution. In this paper, we present an approach for representing events and monitor their execution. In particular, discuss the exceptions that can occur during an event execution and how they can be managed using event management rules. We present strategies for writing management rules that are used to handle problematic events and to build a DAG based programming system for event management. Our simulation results show how the performance of our event management system performs with the exception management rules.

Many exponentially-hard problems can be solved by searching through a space of states to determine a sequence of steps constituting a solution. Algorithms that produce optimal solutions (e.g., shortest path) generally require greater computational resources (e.g., time) than their sub-optimal counterparts. Consequently, many optimal algorithms cannot produce any usable solution when the amount of time available is limited or hard to predict in advance. Anytime algorithms address this problem by initially finding a suboptimal solution very quickly and then generating incrementally better solutions with additional time, effectively providing the best solution generated so far anytime it is required. In this research, we generate initial solutions cheaply using a fast search algorithm. We then improve this low-quality solution by identifying subsequences of steps that appear, based on heuristic estimates, to be considerably longer than necessary. Finally, we perform a more expensive search between the endpoints of each subsequence to find a shorter connecting path. We will show that this improves the overall solution incrementally over time while always having a valid solution to return whenever time runs out. We present results that demonstrate in several problem domains that AIRS (Anytime Iterative Refinement of a Solution) rivals other widely used and recognized anytime algorithms and also produces results comparable to other popular (but not anytime) heuristic algorithms such as Bidirectional A* search.

Many studies have been conducted on how to detect emotion classes or magnitudes from multimedia information such as text, audio, and images. However, the methods that can use predicted emotion classes and magnitudes to render emotion expressions in Embodied Conversational Agents (ECA) are still unclear. This paper proposes a computer graphics methodology that uses predicted non-linear regression values to render facial expressions using mesh morphing techniques. Results of the rendering technique are presented and discussed.

Robots are becoming increasingly common in home, industrial and medical environments. Their end users may know what they want the robots to do but lack the required technical skills to program them. We present a case-based reasoning approach for training a control module that controls a multi-purpose robotic platform. The control module learns by observing an expert performing a task and does not require any human intervention to program or modify the control module. To avoid requiring the control module to be modified when the robot it controls is repurposed, smart sensors and effectors register with the control module allowing it to dynamically modify the case structure it uses and how those cases are compared. This allows the hardware configuration to be modified, or completely changed, without having to change the control module. We present a case study demonstrating how a robot can be trained using learning by observation and later repurposed with new sensors and then retrained.

Search algorithms such as Rapidly-exploring Random Trees (RRTs) are common in robot programming. Including graphical representations of the output of these algorithms in a robotics framework can make the algorithms more accessible to students, and can also help programmers analyze and account for unexpected results. For this project, we used the Tekkotsu open source robot programming framework, available at Tekkotsu.org. We extended Tekkotsu’s graphical user interface for displaying vision data and maps to also display the output of an RRT search. We created several demos using two types of searches: one from a navigation path planner, and one from an arm path planner. In some cases the search had no solution, and the graphical output helped to illustrate why. This confirms the utility of the RRT visualization for explaining unexpected search results. We expect that this tool will also contribute to improved student understanding of the search algorithm.

We present a framework to represent and reason about nar- ratives. We build a symbolic probabilistic representation of the temporal sequence of world states and events implied by a narrative using statistical approaches. We show that the combination of this representation together with domain knowledge and symbolic probabilistic reasoning algorithms enables understanding of a narrative and answering semantic questions whose responses are not contained in the narrative. In our experiments, we show the power of our framework (vs. traditional approaches) in answering semantic questions for two domains of RoboCup soccer commentaries and early reader children stories focused on spatial contexts.

Sparse distributed memory is an auto-associative memory system that stores high dimensional Boolean vectors. Here we present an extension of the original SDM, the Integer SDM that uses modular arithmetic integer vectors rather than binary vectors. This extension preserves many of the desirable properties of the original SDM: auto-associativity, content addressability, distributed storage, and robustness over noisy inputs. In addition, it improves the representation capabilities of the memory and is more robust over normalization. It can also be extended to support forgetting and reliable sequence storage.