Since the seminal work of Sampath et al. in 1996, despite the subsequent flourishing of techniques on diagnosis of discrete-event systems (DESs), the basic notions of fault and diagnosis have been remaining conceptually unchanged. Faults are defined at component level and diagnoses incorporate the occurrences of component faults within system evolutions: diagnosis is context-free. As this approach may be unsatisfactory for a complex DES, whose topology is organized in a hierarchy of abstractions, we propose to define different diagnosis rules for different subsystems in the hierarchy. Relevant fault patterns are specified as regular expressions on patterns of lower-level subsystems. Separation of concerns is achieved and the expressive power of diagnosis is enhanced: each subsystem has its proper set of diagnosis rules, which may or may not depend on the rules of other subsystems. Diagnosis is no longer anchored to components: it becomes context-sensitive. The approach yields seemingly contradictory but nonetheless possible scenarios: a subsystem can be normal despite the faulty behavior of a number of its components (positive paradox); also, it can be faulty despite the normal behavior of all its components (negative paradox).

Semi-supervised learning algorithms commonly incorporate the available background knowledge such that an expression of the derived model's quality is improved. Depending on the specific context quality can take several forms and can be related to the generalization performance or to a simple clustering coherence measure. Recently, a novel perspective of semi-supervised learning has been put forward, that associates semi-supervised clustering with the efficiency of spectral methods. More precisely, it has been demonstrated that the appropriate use of partial supervision can bias the data Laplacian matrix such that the necessary eigenvector computations are provably accelerated. This result allows data mining practitioners to use background knowledge not only for improving the quality of clustering results, but also for accelerating the required computations. In this paper we initially provide a high level overview of the relevant efficiency maximizing semi-supervised methods such that their theoretical intuitions are comprehensively outlined. Consecutively, we demonstrate how these methods can be extended to handle multiple clusters and also discuss possible issues that may arise in the continuous semi-supervised solution. Finally, we illustrate the proposed extensions empirically in the context of text clustering.

Humans have developed jurisprudence as a mechanism to solve conflictive situations by using past experiences. Following this principle, we propose an approach to enhance a multi-agent system by adding an authority which is able to generate new regulations whenever conflicts arise. Regulations are generated by learning from previous similar situations, using a machine learning technique (based on Case-Based Reasoning) that solves new problems using previous experiences. This approach requires: to be able to gather and evaluate experiences; and to be described in such a way that similar social situations require similar regulations. As a scenario to evaluate our proposal, we use a simplified version of a traffic scenario, where agents are traveling cars. Our goals are to avoid collisions between cars and to avoid heavy traffic. These situations, when happen, lead to the synthesis of new regulations. At each simulation step, applicable regulations are evaluated in terms of their effectiveness and necessity. Overtime the system generates a set of regulations that, if followed, improve system performance (i.e. goal achievement).

My research seeks insight into the complexity of computationalreasoning under uncertain information. I focus onpreference aggregation and social choice. Insights in theseareas have broader impacts in the areas of complexity theory, autonomous agents, and uncertainty in artificial intelligence.

While voting schemes provide an effective means for aggregating preferences, methods for the effective elicitation of voter preferences have received little attention. We address this problem by first considering approximate winner determination when incomplete voter preferences are provided. Exploiting natural scoring metrics, we use max regret to measure the quality or robustness of proposed winners, and develop polynomial time algorithms for computing the alternative with minimax regret for several popular voting rules. We then show how minimax regret can be used to effectively drive incremental preference/vote elicitation and devise several heuristics for this process. Despite worst-case theoretical results showing that most voting protocols require nearly complete voter preferences to determine winners, we demonstrate the practical effectiveness of regret-based elicitation for determining both approximate and exact winners on several real-world data sets.

Bounded suboptimal search algorithms offer shorter solving times bysacrificing optimality and instead guaranteeing solution costs withina desired factor of optimal. Typically these algorithms use a singleadmissible heuristic both for guiding search and bounding solutioncost. In this paper, we present a new approach to bounded suboptimalsearch, Explicit Estimation Search, that separates these roles,consulting potentially inadmissible information to determine searchorder and using admissible information to guarantee the cost bound.Unlike previous proposals, it successfully combines estimates ofsolution length and solution cost to predict which node will lead mostquickly to a solution within the suboptimality bound. An empiricalevaluation across six diverse benchmark domains shows that ExplicitEstimation Search is competitive with the previous state of the art indomains with unit-cost actions and substantially outperformspreviously proposed techniques for domains in which solution cost andlength can differ.

We propose an online classification approach for co-occurrence data which is based on a simple information theoretic principle. We further show how to properly estimate the uncertainty associated with each prediction of our scheme and demonstrate how to exploit these uncertainty estimates. First, in order to abstain highly uncertain predictions. And second, within an active learning framework, in order to preserve classification accuracy while substantially reducing training set size. Our method is highly efficient in terms of run-time and memory footprint requirements. Experimental results in the domain of text classification demonstrate that the classification accuracy of our method is superior or comparable to other state-of-the-art online classification algorithms.

To encompass them, several extensions of Dung's argumentation framework (AF) [Dung, This extended research abstract describes an 1995] have been proposed, but the most general, as shown in argumentation-based approach to modelling articulated [Baroni et al., 2011], is the Argumentation Framework with decision making contexts. The approach Recursive Attacks (AF RA) formalism [Baroni et al., 2009b; encompasses a variety of argument and attack 2011]. In[Baroni et al., 2009a; 2010b] we showed how to organise schemes aimed at representing basic knowledge arguments that are instances of argument schemes in and reasoning patterns for decision support.

In many multi-agent systems, the emergence of norms is the primary factor that determines overall behavior and utility. Agent simulations can be used to predict and study the development of these norms. However, a large number of simulations is usually required to provide an accurate depiction of the agents’ behavior, and some rare contingencies may still be overlooked completely. The cost and risk involvedwith agent simulations can be reduced by analyzing a system theoretically and producing models of its behavior. We use such a theoretical approach to examine the dynamics of a population of agents playing a coordination game to determine all the norms to which the society can converge, and develop a system of linear recurrence relations that predict how frequently each of these norms will be reached, as well as the average convergence time. This analysis produces certain guarantees about system behavior that canot be provided by a purely empirical approach, and can be used to make predictions about the emergence of norms that numerically match those obtained through large-scale simulations.

Plan recognition is the problem of inferring the goals and plans of an agent from partial observations of her behavior. Recently, it has been shown that the problem can be formulated and solved using planners, reducing plan recognition to plan generation. In this work, we extend this model-based approach to plan recognition to the POMDP setting, where actions are stochastic and states are partially observable. The task is to infer a probability distribution over the possible goals of an agent whose behavior results from a POMDP model. The POMDP model is shared between agent and observer except for the true goal of the agent that is hidden to the observer. The observations are action sequences O that may contain gaps as some or even most of the actions done by the agent may not be observed. We show that the posterior goal distribution P ( G | O ) can be computed from the value function V G ( b ) over beliefs b  generated by the POMDP planner for each possible goal G. Some extensions of the basic framework are discussed, and a number of experiments are reported.