We present and implement a Weighted Partial MaxSAT solver based on successive calls to a SAT solver. We prove the correctness of our algorithm and compare our solver with other Weighted Partial MaxSAT solvers.

Distributions over permutations arise in applications ranging from multi-object tracking to ranking of instances. The difficulty of dealing with these distributions is caused by the size of their domain, which is factorial in the number of considered entities ($n!$). It makes the direct definition of a multinomial distribution over permutation space impractical for all but a very small $n$. In this work we propose an embedding of all $n!$ permutations for a given $n$ in a surface of a hypersphere defined in $\mathbbm{R}^{(n-1)^2}$. As a result of the embedding, we acquire ability to define continuous distributions over a hypersphere with all the benefits of directional statistics. We provide polynomial time projections between the continuous hypersphere representation and the $n!$-element permutation space. The framework provides a way to use continuous directional probability densities and the methods developed thereof for establishing densities over permutations. As a demonstration of the benefits of the framework we derive an inference procedure for a state-space model over permutations. We demonstrate the approach with applications.

Density modeling is notoriously difficult for high dimensional data. One approach to the problem is to search for a lower dimensional manifold which captures the main characteristics of the data. Recently, the Gaussian Process Latent Variable Model (GPLVM) has successfully been used to find low dimensional manifolds in a variety of complex data. The GPLVM consists of a set of points in a low dimensional latent space, and a stochastic map to the observed space. We show how it can be interpreted as a density model in the observed space. However, the GPLVM is not trained as a density model and therefore yields bad density estimates. We propose a new training strategy and obtain improved generalisation performance and better density estimates in comparative evaluations on several benchmark data sets.

Creating artificial intelligent agents that are high-fidelity simulations of natural agents will require the engagement of behavioral scientists. However, agent programming systems that are accessible to behavioral scientists are too limited to create rich agents, and systems for creating rich agents are accessible mainly to computer scientists, not behavioral scientists. We are solving this problem by engaging behavioral scientists in the design of a programming language, and integrating reinforcement learning into the programming language. This strategy will help our language achieve adaptivity, modularity, and, most importantly, accessibility to behavioral scientists. In addition to allowing behavioral scientist to write rich agent programs, our language — AFABL (A Friendly Behavior Language) — will enable a true discipline of modular agent software engineering with broad implications for games, interactive storytelling, and social simulations.

I intend to use RL to bring the two together, The Reinforcement Learning (RL) agent interacts with a dynamical and generate motion from the proposed first principles system whose states capture all the relevant information in realistic biomechanical models, and compare the about the current configuration of the agent and its results to the behavior of living creatures. This is a nontrivial environment. By specifying a sequence of actions, the agent problem: biomechanical models are continuous, highdimensional alters the state transitions of this dynamical system. The optimality and nonlinear, and the optimality criteria considered criterion is formalized by a reward function defined in the literature are non-quadratic. In order to address over state-action pairs, and the agent's goal is to maximize these profound challenges, I propose three basic principles the cumulative reward.

My thesis investigates fault tolerance for cooperative agent systems that have some equivalent of self-replication and self-death. Utilizing biologically-inspired mechanisms, I increase multi-agent system robustness for faulty agents when it is unknown exactly which agent is malfunctioning. It is important to determine new ways to increase robustness of a system, as otherwise it cannot be guaranteed to function in all situations and thus cannot be relied upon. Robustness of a system allows agents to recover from errors and thus function continuously, an increasingly important trait as agent systems are deployed in real world scenarios such as sensor networks or surveillance systems where faulty or malicious nodes could disrupt application performance. To achieve robustness, there must either be prevention of all errors, or a technique for recovering from errors after they have occurred. My thesis creates a new fault tolerance mechanism inspired by cancer biology to remove faulty agents, and then re-applies the developed technique to study the removal of biological cancer cells in simulation.

Low-level direct commanding of space robots can be time consuming or impractical for complex systems with many degrees of freedom. My research will adaptively raise the level of interaction between the operator and the robot by (1) allowing the robot to learn implicit plans by detecting patterns in the interaction history, and (2) enabling the human to demonstrate continuous motions through teleoperation. Learned tasks and plans are recorded for future use. I introduce a novel representation of continuous actions called parameterized probabilistic flow tubes that I hypothesize will more closely encode a human's intended motions and provide flexibility during execution in new situations. I also introduce the use of planning for plan recognition in the domain of hybrid tasks.

Area coverage is one of the emerging problems in multi-robot coordination. In this task a team of robots is cooperatively trying to observe or sweep an entire area, possibly containing obstacles, with their sensors or actuators. The goal is to build an efficient path for each robot which jointly ensure that every single point in the environment can be seen or swept by at least one of the robots while performing the task.

We propose a system that bridges the gap between the two major approaches toward natural language processing: robust shallow text processing and domain-specific (often linguistically-based) deep understanding. We propose to use an existing linguistically motivated deep understanding system as the core and to leverage statistical techniques and external resources such as world knowledge to broaden coverage and increase robustness. We will also develop a semantic representation framework, which supports underspecification, granularity and incrementality, the critical factors of robustness in representing natural language semantics.

I present skill bootstrapping, a proposed new research direction for agent learning and planning that allows an agent to start with low-level primitive actions, and develop skills that can be used for higher-level planning. Skills are developed over the course of solving many different problems in a domain, using reinforcement learning techniques to complement the benefits and disadvantages of heuristic-search planning. I describe the overall architecture of the proposed approach and discuss how it relates to other work.