Results from behavioral economics show that individuals do not always maximize monetary payoffs. Within behavioral economics different models of social preference have been put forth to account for this deviation from standard assumptions of game theory and economics. Incorporating such models into agent decision making is increasingly relevant to design systems which interact with or on behalf of humans. Existing models, which correctly predict outcomes across a large set of games, are fairly complex. To this end, we present aspiration based social preference model and evaluate it by considering two player dilemma games. We show that the qualitative predictions of our model are consistent with results from behavioral economics.

Robot factors such as motions and utterances have a possibility of interaction effects with generation and other human factors, and these effects influence robotics design in elder care. Some psychological experiments conducted in our research group found these interaction effects between generation and other factors based on directly comparison between younger and elder persons in interaction with a small-sized humanoid robot. The paper firstly reviews the previous two studies, reports results of the current experiment, and then discusses about their implications from the perspective of robotics design for elder care.

A general perceptual model is proposed for Eldercare Robot implementation that is comprised of audition functionality interconnected with a feedback-driven perceptual reasoning agent. Using multistage signal analysis to feed temporally tiered learning/recognition modules, concurrent access to sound event localization, classification, and context is realized. Patterns leading to the quantification of patient emotion/well being can be inferred using a perceptual reasoning agent. The system is prototyped using a Nao H-25 humanoid robot with an online processor running the Nao Qi SDK and the Max/MSP environment with the FTM, and GF libraries.

Quality assurance remains a key topic in human computation research field. Prior work indicates independent agreement is effective for low difficulty tasks, but has limitations. This paper addresses this problem by proposing a tournament selection based quality control process. The experimental results from this paper show that the human are better at identifying the correct answers than producing them themselves.

Recognizing human activities from wearable sensor data is an important problem, particularly for health and eldercare applications. However, collecting sufficient labeled training data is challenging, especially since interpreting IMU traces is difficult for human annotators. Recently, crowdsourcing through services such as Amazon's Mechanical Turk has emerged as a promising alternative for annotating such data, with active learning serving as a natural method for affordably selecting an appropriate subset of instances to label. Unfortunately, since most active learning strategies are greedy methods that select the most uncertain sample, they are very sensitive to annotation errors (which corrupt a significant fraction of crowdsourced labels). This paper proposes methods for robust active learning under these conditions. Specifically, we make three contributions: 1) we obtain better initial labels by asking labelers to solve a related task; 2) we propose a new principled method for selecting instances in active learning that is more robust to annotation noise; 3) we estimate confidence scores for labels acquired from MTurk and ask workers to relabel samples that receive low scores under this metric. The proposed method is shown to significantly outperform existing techniques both under controlled noise conditions and in real active learning scenarios. The resulting method trains classifiers that are close in accuracy to those trained using ground-truth data.

MapReduce provided a novel computing model for complex job decomposition and sub-tasks management to support cloud computing with large distributed data sets. However, its performance is significantly influenced by the working data distributions over those data sets. In this paper, we put forward a novel model to balance data distribution to improve cloud computing performance in data-intensive applications, such as distributed data mining. By extending the classic MapReduce model with an agent-aid layer and abstracting working load requests for data blocks as tokens, the agents can reason from previously received tokens about where to send other tokens in order to balance the working tasks and improve system performance. Our key contribution lies in building an efficient token routing algorithm in spite of agents' unknowing to the global state of data distribution in cloud. We also built a prototype of our system, and the experimental results show that our approach can significantly improve the efficiency of cloud computing.

We present a model of interactive activity recognition and prompting for use in an assistive system for persons with cognitive disabilities. The system can determine the user’s state by interpreting sensor data and/or by explicitly querying the user, and can prompt the user to begin or end tasks. The objective of the system is to help the user maintain a daily schedule of activities while minimizing interruptions from questions or prompts. The model is built upon an option-based hierarchical POMDP. Options can be programmed and customized to specify complex routines for prompting or questioning. Novel aspects of the model include (1) the introduction of adaptive options, which employ a lightweight user model and are able to provide near-optimal performance with little exploration; (2) a restricted-inquiry dual-control algorithm that can appeal for help from the user when sensor data is ambiguous; and (3) a combined filtering / most likely-sequence algorithm for activities determining the beginning and ending time points of the user’s activities. Experiments show that each of these features contributes to the robustness of the model.

The use of computer mediated communication has resulted in a new form of written text--Microtext--which is very different from well-written text. Tweets and SMS messages, which have limited length and may contain misspellings, slang, or abbreviations, are two typical examples of microtext. Microtext poses new challenges to standard natural language processing tools which are usually designed for well-written text. The objective of this work is to normalize microtext, in order to produce text that could be suitable for further treatment. We propose a normalization approach based on the source channel model, which incorporates four factors, namely an orthographic factor, a phonetic factor, a contextual factor and acronym expansion. Experiments show that our approach can normalize Twitter messages reasonably well, and it outperforms existing algorithms on a public SMS data set.

While microblogging has gained popularity on the Internet, analyzing and processing short messages has become a challenging task in natural language processing. This paper analyzes the differences between Internet short messages (or “microtext”) and general articles by comparing the Plurk Corpus and the Sinica Balanced Corpus. Likelihood ratio and the tóngyìcícílín thesaurus are adopted to analyze the lexical semantics of frequent terms in each corpus. Furthermore, the NTUSD sentiment dictionary is used to compare the sentiment distribution of the two corpora. The result is also applied to sentiment transition analysis.

We present in this paper, the application of a novel approach to computational modeling, understanding and detection of social phenomena in online multi-party discourse. A two-tiered approach was developed to detect a collection of social phenomena deployed by participants, such as topic control, task control, disagreement and involvement. We discuss how the mid-level social phenomena can be reliably detected in discourse and these measures can be used to differentiate participants of online discourse. Our approach works across different types of online chat and we show results on two specific data sets.