Ironic utterances promise an expected meaning that never arrives, and deliver instead a meaning that exposes the failure of our expectations. Though they can appear contextually inappropriate, ironic statements succeed when they subvert their context of use, so it is the context rather than the utterance that is shown to be incongruous. Every ironic statement thus poses two related questions: the first, “what is unexpected about my meaning?” helps us answer the second, “what is unexpected about my context of use?”. Like metaphor, irony is not overtly marked, and relies instead on a listener’s understanding of stereotypical norms to unpack its true meaning. In this paper we consider how irony relies upon and subverts our stereotypical knowledge of a domain, and show how this knowledge can be exploited to both recognize and generate ironic similes for a topic.

We aim to identify and control unintentional humor occurring in human-computer interaction, and recreate it intentionally. In this research we focus on text prediction systems, a type of interactive programs employed in mobile phones, search engines, and word processors. More specifically, we identified two design principles, inspired by humor and emotion theories, and implemented them in a proof-of-concept tool simulating a specific type of text prediction.

This study extends humor theory to explain puns originating from orthographic conversion in Japanese computer-mediated communication (CMC). Standard word-processing software converts Romanized input to appropriate orthographic output consisting of phono-graphic kana and ideographic kanji . Such software may produce an output often semantically incongruent with the intended output, which can be humorous. The dataset analyzed here consists of 492 online submissions to the “Humorous Misconversion Contest” held by the Japan Kanji Aptitude Testing Foundation. Since not all misconversions are funny, the study accounts for how misconversions satisfy funniness conditions of the Semantic Script Theory of Humor. The study finds that script interpretability, as the basis of script compatibility, and script opposition are of most importance in humor perception. It also finds that script oppositeness resides not only within texts but also in outer contexts. As yet, very few academic studies have discussed humor in Japanese CMC. Since a majority of verbal humor is researched on alphabet-based languages, the observations here are expected to enrich and broaden our knowledge of humor.

We study the mixtures of factorizing probability distributions represented as visible marginal distributions in stochastic layered networks. We take the perspective of kernel transitions of distributions, which gives a unified picture of distributed representations arising from Deep Belief Networks (DBN) and other networks without lateral connections. We describe combinatorial and geometric properties of the set of kernels and products of kernels realizable by DBNs as the network parameters vary. We describe explicit classes of probability distributions, including exponential families, that can be learned by DBNs. We use these submodels to bound the maximal and the expected Kullback-Leibler approximation errors of DBNs from above depending on the number of hidden layers and units that they contain.

Emotional intelligence is the key for acceptance of intelligent agents by humans as equal partners, e.g., in ad hoc teams. At the same time, its existing implementations in intelligent agents are mostly limited to basic affects. Currently, there is no consensus in the understanding of complex and social emotions at the level of functional and computational models. The approach of this work is based on the mental state formalism, originally developed as a part of the cognitive architecture GMU BICA and recently extended to include affective building blocks (A.V. Samsonovich, AAAI Technical Report WS-12-06: 109-116, 2012). In the present work, complex social emotions like humor, jealousy, compassion, shame, pride, etc. are identified as emergent patterns of appraisals represented by schemas, that capture the cognitive nature of these emotions and enable their modeling. A general model of complex emotions and emotional relationships is constructed that can be validated by simulations of emotionally biased interactions and emergent relationships in small groups of agents. The framework will be useful in cognitive architectures for designing human-like-intelligent social agents possessing a sense of humor and other human-like emotionally intelligent capabilities.

Higher life expectancies lead to an increased prevalenceof dementia in older adults, which is projected torise dramatically in the future. The link between malnutritionand dementia highlights the need to closelymonitor nutrition as early as possible. However, currentself-report assessment methods are labor-intensive,time-consuming and inaccurate. Technology has the potentialof assisting in nutritional analysis by alleviatingthe cognitive load of recording food intake and lesseningthe burden of care for the elderly. Therefore, we proposean intelligent nutritional assessment system thatwill monitor the dietary patterns of older adults with dementiaat their homes. Our computer vision-based systemconsists of food recognition and portion estimationalgorithms that, together, provide nutritional analysisof an image of a meal. We create a novel food imagedataset on which we achieve an 87.2% recognition accuracy.We apply several well-known segmentation andrecognition algorithms and analyze their suitability tothe food recognition problem.

Although decision processes are often described at the individual level of cognition (e.g. Tversky and Kahnemann The particular beliefs instantiated within the model are (1981)), they are subject to social and cultural influences based on a combination of results from empirical studies at both the interpersonal and societal levels. The adoption of technology adoption by Venkatesh et al. (2003). of new technology depends on various factors, such The UTAUT model combines eight of the most prominent as the type of technology, the context or culture in which technology-acceptance models observed in the literature and the technology is introduced, and the individual decisions provides a definitive list of variables that are critically relevant by people within that culture, as most individuals evaluate to an individual's Behavioral Intention (BI) and Use Behavior an innovation from the subjective evaluations of peers who (UB) for adopting a new technology, including Performance have adopted an innovation (see Watts and Dodds (2007) Expectancy (PE), Effort Expectancy (EE), Social for a discussion of network-diffused influence). These influences Influence (SI), Facilitating Conditions (FC), and Voluntariness propagate through the social network as a function of Use (VoU). of agent interactions.

Many strategic actions carry a "contagious" component beyond the immediate locale of the effort itself. Viral marketing and peacekeeping operations have both been observed to have a spreading effect. In this work, we use counterinsurgency as our illustrative domain. Defined as the effort to block the spread of support for an insurgency, such operations lack the manpower to defend the entire population and must focus on the opinions of a subset of local leaders. As past researchers of security resource allocation have done, we propose using game theory to develop such policies and model the interconnected network of leaders as a graph. Unlike this past work in security games, actions in these domains possess a probabilistic, non-local impact. To address this new class of security games, recent research has used novel heuristic oracles in a double oracle formulation to generate mixed strategies. However, these heuristic oracles were evaluated only on runtime and quality scaling with the graphsize. Given the complexity of the problem, numerous other problem features and metrics must be considered to better inform practical application of such techniques. Thus, this work provides a thorough experimental analysis including variations of the contagion probability average and standard deviation. We extend the previous analysis to also examine the size of the action set constructed in the algorithms and the final mixed strategies themselves. Our results indicate that game instances featuring smaller graphs and low contagion probabilities converge slowly while games with larger graphs and medium contagion probabilities converge most quickly.

Network analysis is a growing field across many domains, including computer vision, social media marketing, transportation networks, and intelligence analysis. The growing use of digital communication devices and platforms, as well as persistent surveillance sensors, has resulted in explosion of the quantity of data and stretched the abilities of current technologies to process this data and draw meaningful conclusions. Current tools either require significant levels of manual intervention (e.g., to prepare the data, to define patterns, or to draw conclusions from data) or are unable to generalize to new data sources and analysis needs. In this paper, we present automated solutions to two major problems in network analysis: (a) finding patterns in the network data that contains high levels of noise and irrelevant information; and (b) learning repetitive patterns and dependencies between entities and attributes. Our modeling framework represents network data using multi-attributed graphs that can encode various discrete and continuous features and relationships between network entities. The pattern search and learning model is based on probabilistic multi-attributed graph matching, and implemented using distributed message passing algorithms. Our algorithms achieved high accuracy rates in learning and finding patterns in the data, are flexible to new domains and data types, and scale to large datasets using the Map-Reduce framework.

In the real world we observe a proliferation of regional dialects and jargons. Most of the research on naming conventions focuses on how to explain the process that allows a single naming convention to establish itself. This paper presents a different approach that aims to investigate why different conventions may emerge and coexist for a certain amount of time. The naming game is an abstraction of lexical acquisition dynamics, in which n agents try to find an agreement on the names to give to objects. To understand how different heterogeneous conventions emerge, I discuss a naming game model that takes into account experimental data on human and animal learning.