Homophily and stochastic equivalence are two primary features of interest in social networks. Recently, the multiplicative latent factor model (MLFM) is proposed to model social networks with directed links. Although MLFM can capture stochastic equivalence, it cannot model well homophily in networks. However, many real-world networks exhibit homophily or both homophily and stochastic equivalence, and hence the network structure of these networks cannot be modeled well by MLFM. In this paper, we propose a novel model, called generalized latent factor model (GLFM), for social network analysis by enhancing homophily modeling in MLFM. We devise a minorization-maximization (MM) algorithm with linear-time complexity and convergence guarantee to learn the model parameters. Extensive experiments on some real-world networks show that GLFM can effectively model homophily to dramatically outperform state-of-the-art methods.

One drawback with using plan recognition in adversarial games is that often players must commit to a plan before it is possible to infer the opponent's intentions. In such cases, it is valuable to couple plan recognition with plan repair, particularly in multi-agent domains where complete replanning is not computationally feasible. This paper presents a method for learning plan repair policies in real-time using Upper Confidence Bounds applied to Trees (UCT). We demonstrate how these policies can be coupled with plan recognition in an American football game (Rush 2008) to create an autonomous offensive team capable of responding to unexpected changes in defensive strategy. Our real-time version of UCT learns play modifications that result in a significantly higher average yardage and fewer interceptions than either the baseline game or domain-specific heuristics. Although it is possible to use the actual game simulator to measure reward offline, to execute UCT in real-time demands a different approach; here we describe two modules for reusing data from offline UCT searches to learn accurate state and reward estimators.

The most prominent way to establish trust in online markets such as eBay are reputation systems that publish buyer feedback about a seller's past behavior. These systems, however, critically rely on assumptions that are rarely met in real-world marketplaces: first, it is assumed that there are no reporting costs and no benefits from lying so that buyers honestly report their private experiences. Second, it is assumed that every seller is long-lived, i.e. will continue to trade on the marketplace indefinitely and, third, it is assumed that sellers cannot whitewash, i.e. create new accounts once an old one is ran down. In my thesis, I address all of these assumptions and design incentive-compatible trust mechanisms that do not rely on any of the aforementioned assumptions. Moreover, I focus on designs that minimize common knowledge assumptions with respect to the players' valuations, costs and beliefs.

Transfer learning is the process of using knowledge gained while solving one problem to solve a new, previously unencountered problem. Current research has concentrated on analogical transfer - a mechanic is able to fix a type of car he has never seen before by comparing it to cars he has fixed before. This approach is typical of case-based reasoning systems and has been successful on a wide variety of problems [Watson, 1997]. When a new problem is encountered, a database of previously solved problems is searched for a problem with similar features. The solution to the most similar problem is selected, adapted and then applied to the new problem. Similar methods exist for adapting reinforcement learning policies [Taylor and Stone, 2009]. We refer to the above approaches as solution adaptation algorithms - a pair of problems are matched on similarity and the solution to the first problem, after some adaptation, is applied to the second problem. The solution adaptation approach requires three things.

Multi-agent path planning is a challenging problem with numerous real-life applications, including robotics, logistics, military operations planning, disaster rescue, and computer games. We look at navigating large numbers of mobile units to their targets on navigation graphs such as grid maps. The size of problems examined is significantly larger than can be handled using optimal multi-agent pathfinding algorithms in practice. We introduced MAPP, a tractable algorithm for multi-agent path planning on undirected graphs. MAPP and its extended versions are complete on well specified and tractably testable classes of problems. They have low-polynomial worst-case upper bounds for the running time, the memory requirements, and the length of solutions. Experiments on realistic game grid maps, with uniformly randomly generated start and target locations for each unit, show MAPP as a state-of-the-art multi-agent pathfinding algorithm in terms of scalability and success ratio (i.e., percentage of solved units). Even on challenging scenarios with 2000 units, MAPP solves 92% to 99.7% of units. FAR and WHCA*, two fast but incomplete algorithms that were previously state-of-the-art in terms of scalability, solve as few as 17.5% and 12.3% of these problems. The quality of MAPP's solutions is empirically analyzed using multiple quality criteria: total travel distance, makespan, and sum of actions (including move and wait actions). MAPP is competitive in terms of solution quality and speed with FAR and WHCA*. MAPP further provides the formal characterizations that FAR and WHCA* lack, on problems it can solve as well as low-polynomial upper bounds on the resources required. As optimal algorithms have limited scalability, we evaluated the solution quality of suboptimal algorithms using lower bounds of optimal values. We showed that MAPP's solutions have a reasonable quality. For example, MAPP's total travel distance is on average 19% longer than a lower bound on the optimal value.

A baby experiencing the world for the first time faces a considerable challenging sorting through what William James called the "blooming, buzzing confusion" of the senses. With the increasing capacity of modern sensors and the complexity of modern robot bodies, a robot in an unknown or unfamiliar body faces a similar and equally daunting challenge. Addressing this challenge directly by designing robot agents capable of resolving the confusion of sensory experience in an autonomous manner would substantially reduce the engineering required to program robots and the improve the robustness of resulting robot capabilities. Working towards a general solution to this problem, this work uses distinctive state abstractions and sensorimotor embedding to generate basic knowledge of sensor structure, local geometry, and object geometry starting with uninterpreted sensors and effectors.

Human Behavior Analysis in Uncontrolled Environmentscan be categorized in two main challenges:1) Feature extraction and 2) Behavior analysisfrom a set of corporal language vocabulary. Inthis work, we present our achievements characterizingsome simple behaviors from visual data ondifferent real applications and discuss our plan forfuture work: low level vocabulary definition frombag-of-gesture units and high level modelling andinference of human behaviors.

In this paper, I present my ongoing research on temporal defeasible argumentation-based multi-agent planning. In multi-agent planning a team of agents share a set of goals but have diverse abilities and temporal beliefs, which vary over time. In order to plan for these goals, agents start a stepwise dialogue consisting of exchanges of temporal plan proposals, plus temporal arguments against them, where both, actions with different duration, and temporal defeasible arguments, need to be integrated. This thesis proposes a computational framework for this research on multi-agent planning.

Unfortunately, this example is a useful analogy can be treated within a unified framework along for many situations in real life, where (individually) rational with seemingly unrelated problems in the fields of judgment behavior leads to a disaster for the society.

Temporal regulations are needed to express commitments The proposal of Elisa Marengo's thesis is to extend to achieve something and in a specified order. For commitment protocols in order to (i) allow for expressing instance, an insurance company commits to paying an innetwork commitments to temporal regulations, and surgeon for a procedure only after a covered patient (ii) to supply a tool for expressing laws, conventions has undergone the procedure. Patterns of interaction, instead, and the like, in order to specify legal interactions.