HAPTIC is grounded in game theory and probabilistic modeling. It has been proved that My thesis contributes to the field of multi-agent HAPTIC agents learn other agents' behaviors reliably using systems by proposing a novel trust-based decision direct observations. One shortcoming of HAPTIC is that it model for supply chain management.

This paper describes work related to stochastic modeling and decision-theoretic (DT) planning methods applicable to the real-world domain of academic advising.

Quantitative prediction problems involving both spatial and temporal components have appeared prominently in several disparate research areas including finance, supply chain management, and civil engineering. Unfortunately, either the spatial or temporal aspect tends to dominate the other in many prediction formulations. We briefly examine the underlying formulations used in spatial and temporal prediction. Then, we outline a method that combines these approaches and improves prediction results in high-dimensional economic domains by integrating multivariate and time series techniques which require minimal tuning but achieve superior performance compared to previous methods. We present preliminary results in the context of the Trading Agent Competition for Supply Chain Management.

The counterfactual regret minimization (CFR) algorithm is state-of-the-art for computing strategies in large games and other sequential decision-making problems. Little is known, however, about CFR in games with more than 2 players. This extended abstract outlines research towards a better understanding of CFR in multiplayer games and new procedures for computing even stronger multiplayer strategies. We summarize work already completed that investigates techniques for creating "expert" strategies for playing smaller sub-games, and work that proves CFR avoids classes of undesirable strategies. In addition, we provide an outline of our future research direction. Our goals are to apply regret minimization to the problem of playing multiple games simultaneously, and augment CFR to achieve effective on-line opponent modelling of multiple opponents. The objective of this research is to build a world-class computer poker player for multiplayer Limit Texas Hold'em.

Recently, the use of social and human computing has witnessed increasing interest in the AI community. However, in order to harness the true potential of social computing, human subjects must play an active role in achieving computation in social networks and related media. Our work proposes an initial desiderata for effective social computing, drawing inspiration from artificial intelligence. Extensive experimentation reveals that several open issues and research questions have to be answered before the true potential of social and human computing is achieved. We, however, take a somewhat novel approach, by implementing a social networks environment where human subjects cooperate towards computational problem solving. In our social environment, human and artificial agents cooperate in their computation tasks,which may lead to a single problem-solving social network that potentially allows seamless cooperation among human and machine agents.

One of the main research problems in Structural Bioinformatics is the analysis and prediction of three-dimensional structures (3-D) of polypeptides or proteins. The 1990’s Genome projects resulted in a large increase in the number of protein sequences. However, the number of identified 3-D protein structures has not followed the same trend.The determination of protein structure is experimentally expensive and time consuming. This makes scientists largely dependent on computational methods that can predict correct 3-D protein structures only from extended and full amino acid sequences. Several computational methodologies and algorithms have been proposed as a solution to the Protein Structure Prediction (PSP) problem. We briefly describe the AI techniques we have been used to tackle this problem.

Behaviour recognition aims to infer the particular behaviours of the inhabitant in a smart home from a series of sensor readings from around the house. There are many reasons to recognise human behaviours; one being to monitor the elderly or cognitively impaired and detect potentially dangerous behaviours. We view the behaviour recognition problem as the task of mapping the sensory outputs to a sequence of recognised activities. This research focuses on the development of machine learning methods to find an approximation to the mapping between sensor outputs and behaviours. However, learning the mapping raises an important issue, which is that the training data is not necessarily annotated with exemplar behaviours of the inhabitant. This doctoral study takes several steps towards addressing the problem of finding an approximation to this mapping, beginning with separate investigations on current methods proposed in the literature, identifying useful sensory outputs for behaviour recognition, and concluding by proposing two directions: one using supervised learning on annotated sensory stream and one using unsupervised learning on unannotated ones.

My work focuses on building computational agents that assist people in managing their activities in environments in which tempo and complexity outstrip people’s cognitive capacity,such as in coordinating rescue teams in the aftermath of a disaster, or in helping people with dementia manage their everyday lives. A critical challenge faced in such environments is not only that individuals must factor complicated constraints into deciding how and when to act on their own goals, but also that their decisions are further constrained by choices made by others with whom they interact, such as between cooperating teams in disaster relief or between patients and caregivers in an assisted-living facility. An additional challenge in such situations is that the interests of individuals, such as privacy and autonomy, along with slow, costly, uncertain,or otherwise problematic communication may further limitindividuals’ abilities to work together. My work assumes that a computational agent is associated with each individual, and that these agents will work together efficiently to manage individual and joint activities, while maintaining autonomy and privacy to the extent possible.

Many people read online reviews written by other users to learn more about a product or venue. However, the overwhelming amount of user- generated reviews and variance in length, detail and quality across the reviews make it difficult to glean useful information. In this paper, we present a summarization system called Review Spotlight. It provides a brief overview of reviews by using adjective- noun word pairs extracted from the review text. The system also allows the user to click any word pair to read the original sentences from which the word pair was extracted. We present our system implementation as a Google Chrome browser extension, and an evaluation on how two word pair scoring methods (TF and TF-IDF) affect the identification of useful word pairs.

Weighted Pairwise Classification (OWPC) loss [Usunier et al., 2009] which has been shown to be state-of-the-art on Image annotation datasets are becoming larger and (small) text retrieval tasks. WARP uses stochastic gradient larger, with tens of millions of images and tens descent and a novel sampling trick to approximate ranks resulting of thousands of possible annotations. We propose in an efficient online optimization strategy which we a strongly performing method that scales to show is superior to standard stochastic gradient descent applied such datasets by simultaneously learning to optimize to the same loss, enabling us to train on datasets that precision at the top of the ranked list of annotations do not even fit in memory.