Researchers have recognized the importance of classifying personality through discourse for many years. However, this line of research tends to focus almost exclusively on the personality categories known as the Big Five factors. Though this information is certainly valuable, it may also be useful to categorize personality based on the Leary’s Interpersonal Circumplex model which emphasizes a predictive function. In this paper we construct the data set for personality annotation among six dimensions (based on a coding scheme developed from Leary’s Interpersonal Circumplex) for players using a chat interaction in an epistemic game, Land Science. Our results indicate that overall personality annotation is reliable (Average Kappa = 0.65) with the highest reliability for the competitive dimension and the lowest reliability for the leading dimension.

Markov logic networks have been successfully applied to many problems in AI. However, the computational complexity of the inference procedures has limited their application. Previous work in lifted inference, lazy inference and cutting plane inference has identified cases where the entire ground network need not be constructed. These approaches are specific to particular inference procedures, and apply well only to certain classes of problems. We introduce a method of focused grounding that can use either general purpose or domain specific heuristics to produce only the most relevant ground formulas. Though a solution to the focused grounding is not, in general, a solution to the complete grounding, we show empirically that the smaller search space of a focused grounding makes it easier to locate a good solution. We evaluate focused grounding on two diverse domains, joint entity resolution and abductive plan recognition. We show improved results and decreased computation cost for the entity resolution domain relative to a complete grounding. Focused grounding in abductive plan recognition produces state of the art results in a domain where complete grounding proved intractable.

Recommender systems assist users in the daunting task of sifting through large amounts of data in order to select relevant information or items. Common examples include consumer products and services, such as for songs, books, articles, etc. Unfortunately, such systems may be subject to attack by malicious users who want to manipulate the system’s recommendations to suit their needs: to promote their own (or demote a competitor’s) product/service, or to cause disruption in the recommender system. Attacks can cause the recommender system to become unreliable and untrustworthy, resulting in user dissatisfaction. Developers already face tradeoffs in system efficiency and accuracy, and designing for robustness adds an additional dimension for consideration. In this paper, we show how the underlying implementation choices for item-based and user-based Collaborative Filtering recommender systems can affect the accuracy and robustness of recommender systems. We also show how accuracy and robustness can change over a system’s lifetime by analyzing a set of temporal snapshots from system usage over time. Results provide insight into some of the tradeoffs between robustness and accuracy that operators may need to consider in development and evaluation.

Gene selection has become a vital component in the learning process when using high-dimensional gene expression data. Although extensive research has been done towards evaluating the performance of classifiers trained with the selected features, the stability of feature ranking techniques has received relatively little study. This work evaluates the robustness of eleven threshold-based feature selection techniques, examining the impact of data sampling and class noise on the stability of feature selection. To assess the robustness of feature selection techniques, we use four groups of gene expression datasets, employ eleven threshold-based feature rankers, and generate artificial class noise to better simulate real-world datasets. The results demonstrate that although no ranker consistently outperforms the others, MI and Dev show the best stability on average, while GI and PR show the least stability on average. Results also show that trying to balance datasets through data sampling has on average no positive impact on the stability of feature ranking techniques applied to those datasets. In addition, increased feature subset sizes improve stability, but only does so reliably for noisy datasets.

Real-time Strategy (RTS) games are complex domains which are a significant challenge to both human and artificial intelligence (AI). For that reason, and although many AI approaches have been proposed for the RTS game AI problem, the AI of all commercial RTS games is scripted and offers a very static behavior subject to exploits. In this paper, we will focus on a case-based reasoning (CBR) approach to this problem, and concentrate on the process of case-acquisition. Specifically, we will describe 7 different techniques to automatically acquire plans by observing human demonstrations and compare their performance when using them in the Darmok 2 system in the context of an RTS game.

The area of argumentation theory is an increasingly important area of artificial intelligence and mechanisms that are able to automatically detect the argument structure provide a novel area of research. This paper considers the use of kernel methods for argumentation detection and classification. It shows that a classification accuracy of 65%, can be attained using Natural Language Processing based kernel approaches, which do not require any heuristic choice of features.

We consider a cross-cultural interaction scenario where a group of soldiers assisted by robots interact with local vendors in a market place. We develop a model to quantify, analyze and predict the perception of the actions of the soldiers and the robot by the local population. The model assumes that humans are considering collections of concrete and intangible values which are not, in general, directly and linearly convertible into each other. We argue that satisfactory modeling accuracy can be achieved by restricting the considered intangibles to a small set of {\em culture sanctioned social values}. For these values, the culture provides a name, calculation methods, as well as associated rules of conduct. We validate our model by comparing the predicted values with the judgment of a large group of human observers cognizant of the modeled culture. We use the model to evaluate the tradeoffs between several long term strategies to maintain security as well as to increase the trust and goodwill of the local population.

In some relative discussions about the conceptual analysis of translation paradox where people Studies about logic translation could be traced back to (Kolmogorov found the following situation paradoxical with an assumption 1925) (Glivenko 1929) (Gentzen 1933) (Gödel of stronger-weaker distinction about the strength of logics (1933). In this chapter, the discussion on Béziau's case of by weakening the condition of some logical constant on the translation paradox provides an easier way for people purpose: given two logics, one is weaker than the other in to understand how it is possible for people to consider the sense of proving everything the former proves, while at a more general and abstract logic by the bivaluation approach.

The praying mantis uses its front legs for locomotion, prey capture and feeding. Inspired by this dexterity, we began designing a hexapod robot that could use its front legs for both locomotion and manipulation. Our current work focuses on the middle and back legs of the robot. We designed a five degree of freedom leg, using a gimbal to form three intersecting axes of rotation at the hip to imitate a ball-and-socket joint. There is also a one degree of freedom knee, and an unpowered ankle joint. A key requirement for the design is to provide for standing postures in which the robot can support itself without putting any load on the leg servos. This will increase servo life span. We simulated the leg by constructing a 3D model in SolidWorks, then importing that model into the Mirage simulator, part of the Tekkotsu robotics framework. A functioning prototype was then built using Robotis Dynamixel RX-64 servos. This was a geometrically simplified version of the original model, but it retained every motor capability of the original design. We tested the prototype using two types of pre-specified motion sequences, with good results.

Children with cerebral palsy have difficulty moving their hands and muscles due to developmental issues. One way to assist these children is by having them participate in physical therapy. The best form of physical therapy for children is playing. Playing is a natural activity for children, and it also helps in furthering the developments of muscles. This form of therapy is perhaps a greater choice for children because it keeps the child engaged due to the interest the child holds in the activity. By integrating two projects done by previous students, a Pleo that is controlled by a Wii Nunchuk will be able to teach Pleo how to dance. The child will be engaged in this activity for long durations because there are many variations of dance that the Pleo can learn by moving many body parts. Children using this toy will have continuous movement in their arm muscles by moving the Nunchuk for the duration of the activity. This toy will not only help children with severe disabilities feeling equal to their non-disabled peers by allowing them to use controllers found on many game consoles, but it will also enhance the child’s self-esteem and confidence by allowing them to control the outcome of the Pleo.