Such prediction is important to take remedy procedures to increase a team's performance. Existing Teams of voting agents have great potential in finding methods are tailored for specific domains [Ramos and optimal solutions. However, there are fundamental Ayanegui, 2008]. In Nagarajan et al. [2015] I introduce a challenges to effectively use such teams: (i) novel domain independent technique, which learns a prediction selecting agents; (ii) aggregating opinions; (iii) assessing function using only the voting patterns of a team.

Autonomous agents operating in a dynamic environment must be able to reason and make decisions about actions in pursuit of their goals. In addition, in a normative environment an agent's actions are not only directed by the agent's goals, but also by the norms imposed on the agent. Practical reasoning is reasoning about what to do in a given situation, particularly in the presence of conflicts between the agent's practical attitude such as goals, plans and norms. In this thesis we aim: (i) to introduce a model for normative practical reasoning that allows the agents to plan for multiple and potentially conflicting goals and norms at the same time (ii) to implement the model both formally and computationally, (iii) to identify the best plan for the agent to execute by means of argumentation framework and grounded semantics, (iv) to justify the best plan via argumentation-based persuasion dialogue for grounded semantics.

This dissertation develops the intelligent-Coalition Formation framework for Humans and Robots (i-CiFHaR), an intelligent decision making frameworkfor multi-agent coalition formation. i-CiFHaR is a first of its kind that incorporates a library of coalition formation algorithms; employs unsupervised learning to mine crucial patterns among these algorithms; and leverages probabilistic reasoning to derive the most appropriate algorithm(s) to apply in accordance with multiple mission criteria. The dissertation also contributes to the state-of-the-art in swarm intelligence by addressing the search stagnation limitation of existing ant colony optimization algorithms (ACO) by integrating the simulated annealing mechanism. The experimental results demonstrate that the presented hybrid ACO algorithms significantly outperformed the best existing ACO approaches, when applied to three NP-complete optimization problems (e.g., traveling salesman problem, maximal clique problem, multi-agent coalition formation problem).

This thesis focuses on the problem of temporal planning under uncertainty with explicit safety guarantees, which are enforced by means of chance constraints. We aim at elevating the level in which operators interact with autonomous agents and specify their desired behavior, while retaining a keen sensitivity to risk. Instead of relying on unconditional sequences, our goal is to allow contingent plans to be dynamically scheduled and conditioned on observations of the world while remaining safe. Contingencies add flexibility by allowing goals to be achieved through different methods, while observations allow the agent to adapt to the environment. We demonstrate the usefulness of our chance-constrained temporal planning approaches in real-world applications, such as partially observable power supply restoration and collaborative human-robot manufacturing.

In this thesis, we focus on automated advising agents. The advice given is a form of relating recommendations or guidance from an automated agent to its human user. Providing the right advice at the right time is extremely complex, and requires a good adaptation to human desires and changing environments. We propose a novel methodology for designing automated advising agents and evaluate it in three real world environments. Our intelligent advising agents were evaluated through extensive field trials, with hundreds of human subjects. A significant increase in human performance as well as a high level of user satisfaction was recorded when they were equipped with our agents.

In decision-theoretic planning problems, such as (partially observable) Markov decision problems or coordination graphs, agents typically aim to optimize a scalar value function. However, in many real-world problems agents are faced with multiple possibly conflicting objectives. In such multi-objective problems, the value is a vector rather than a scalar, and we need methods that compute a coverage set, i.e., a set of solutions optimal for all possible trade-offs between the objectives. In this project propose new multi-objective planning methods that compute the so-called convex coverage set (CCS): the coverage set for when policies can be stochastic, or the preferences are linear. We show that the CCS has favorable mathematical properties, and is typically much easier to compute that the Pareto front, which is often axiomatically assumed as the solution set for multi-objective decision problems.

My research goal involves simultaneously addressing statistical and computational tradeoffs encountered in modern data analysis and high-dimensional machine learning (eg: hypothesis testing, regression, classification). My future interests include incorporating additional constraints like privacy or communication, and settings involving hidden utilities of multiple cooperative agents or competitive adversaries.

In my doctoral dissertation I investigate patterns appearing in sentences referring to the future. Such patterns are useful in predicting future events. I base the study on a multiple newspaper corpora. I firstly perform a preliminary study to find out that the patterns appearing in future-reference sentences often consist of disjointed elements within a sentence. Such patterns are also usually semantically and grammatically consistent, although lexically variant. Therefore, I propose a method for automatic extraction of such patterns, applying both grammatical (morphological) and semantic information to represent sentences in morphosemantic structure, and then extract frequent patterns, including those with disjointed elements. Next, I perform a series of experiments, in which I firstly train fourteen classifier versions and compare them to choose the best one. Next, I compare my method to the state-of-the-art, and verify the final performance of the method on a new dataset. I conclude that the proposed method is capable to automatically classify future-reference sentences, significantly outperforming state-of-the-art, and reaching 76% of F-score.

Autism Spectrum Disorder is a developmental disorder often characterized by limited social skills, repetitive behaviors, obsessions, and/or routines. Using the small humanoid robot NAO, we designed an interactive program to elicit common social cues from toddlers while in the presence of trained psychologists during standard toddler assessments.  Our program will capture three different videos of the child-robot interaction and create algorithms to analyze the videos and flag autistic behavior to make diagnosis easier for clinicians.  Our novel contributions will be automatic video processing and automatic behavior classification for clinicians to use with toddlers, validated on a large number of subjects and using a reproducible and portable robotic program for the NAO robot.

Earth observation from space allows us to better understand natural phenomenas such as marine currents, to prevent or follow natural disasters, to follow climate evolution and many other things. To achieve that, there are a great number of artificial satellites orbiting Earth, equipped with high-resolution optical instruments and communicating with a network of ground stations. A satellite is said to be agile when it is able to move quickly around its gravity center along its three axes while moving along its orbit, thanks to gyroscopic actuators. It is equipped with a body-mounted optical instrument. To observe a ground area with the instrument, the satellite must be pointed to it. In practice, users submit observation requests to a mission center, which builds activity plans which are sent to the satellites. These plans contain several types of actions such as orbital maneuvers, acquisition realisations and acquisition downloads towards ground stations. Many techniques are used to synthesize such activity plans. Until now, plans are computed offline on the ground and converted into telecommands that the satellite executes strictly, without any flexibility. However, the satellite evolves in a dynamic environment. Unexpected events occur, such as meteorological changes or new urgent observation requests, that the system must handle. Moreover, resource consumption is not always well known. Until now, to ensure that plans will be executable on board with these uncertainties, they are built with worst-case hypothesis on resources consumption. The objective of this work is to give more autonomy to the satellite without compromising the predictability that is needed for some activities. On the ground, we have high computing power and high uncertainty, while on board we have very low computing power and low uncertainty. The main idea is to share decision-making between ground and board to take advantage of the high computing power on the ground and of the low uncertainty on board. First we apply this idea to download scheduling which consists in scheduling file downloads during ground station visibility windows. Second, we apply this idea to observation planning.