Abstract- This paper shows that maintaining logical consistency of an iris recognition system is a matter of finding a suitable partitioning of the input space in enrollable and unenrollable pairs by negotiating the user comfort and the safety of the biometric system. In other words, consistent enrollment is mandatory in order to preserve system consistency. A fuzzy 3-valent disambiguated model of iris recognition is proposed and analyzed in terms of completeness, consistency, user comfort and biometric safety. It is also... More
Category : Artificial Intelligence

  Abstract We present a heuristic search algorithm for solving first-order Markov Decision Processes (FOMDPs). Our approach combines first-order state abstraction that avoids evaluating states individually, and heuristic search that avoids evaluating all states. Firstly, in contrast to existing systems, which start with propositionalizing the FOMDP and then perform state abstraction on its propositionalized version we apply state abstraction directly on the FOMDP avoiding propositionalization. This kind of abstraction is referred to as first-order state abstraction. Secondly, guided by an... More
Category : Artificial Intelligence

  Abstract University timetabling (UTT) is a complex problem due to its combinatorial nature but also the type of constraints involved. The holy grail of (constraint) programming: "the user states the problem the program solves it" remains a challenge since solution quality is tightly coupled with deriving "eective models", best handled by technology experts. In this paper, focusing on the eld of university timetabling, we introduce a visual graphic communication tool that lets the user specify her problem in an... More
Category : Artificial Intelligence

  Abstract We study an approach to policy selection for large relational Markov Decision Processes (MDPs). We consider a variant of approximate policy iteration (API) that replaces the usual value-function learning step with a learning step in policy space. This is advantageous in domains where good policies are easier to represent and learn than the corresponding value functions, which is often the case for the relational MDPs we are interested in. In order to apply API to such problems, we... More
Category : Artificial Intelligence

  Abstract Distributed Constraint Satisfaction (DCSP) has long been considered an important problem in multi-agent systems research. This is because many real-world problems can be represented as constraint satisfaction and these problems often present themselves in a distributed form. In this article, we present a new complete, distributed algorithm called asynchronous partial overlay (APO) for solving DCSPs that is based on a cooperative mediation process. The primary ideas behind this algorithm are that agents, when acting as a mediator, centralize... More
Category : Artificial Intelligence

  Abstract Code optimization and high level synthesis can be posed as constraint satisfaction and optimization problems, such as graph coloring used in register allocation. Graph coloring is also used to model more traditional CSPs relevant to AI, such as planning, time-tabling and scheduling. Provably optimal solutions may be desirable for commercial and defense applications. Additionally, for applications such as register allocation and code optimization, naturally-occurring instances of graph coloring are often small and can be solved optimally. A recent... More
Category : Artificial Intelligence

Abstract A decision process in which rewards depend on history rather than merely on the current state is called a decision process with non-Markovian rewards (NMRDP). In decisiontheoretic planning, where many desirable behaviours are more naturally expressed as properties of execution sequences rather than as properties of states, NMRDPs form a more natural model than the commonly adopted fully Markovian decision process (MDP) model. While the more tractable solution methods developed for MDPs do not directly apply in the presence... More
Category : Artificial Intelligence

  Abstract Recommendation system has been used more and more frequently in many applications recent years. With the increasing information avail- able, not only in quantities but also in types, how to leverage these rich information to build a better recommendation system becomes a natu- ral problem. Most traditional approaches try to design a speci c model for each scenario, which demands great eorts in developing and modi- fying models. In this technical report, we describe our implementation of feature-based... More
Category : Artificial Intelligence

  Abstract This paper addresses the estimation of parameters of a Bayesian network from incomplete data. The task is usually tackled by running the Expectation-Maximization (EM) algorithm several times in order to obtain a high log-likelihood estimate. We argue that choosing the maximum log-likelihood estimate (as well as the maximum penalized log-likelihood and the maximum a posteriori estimate) has severe drawbacks, being affected both by overfitting and model uncertainty. Two ideas are discussed to overcome these issues: a maximum entropy... More
Category : Artificial Intelligence

Tabu search is one of the most ee tive heuristi s for lo ating high-quality solutions to a diverse array of NP-hard ombinatorial optimization problems. Despite the widespread su ess of tabu searh, resear hers have a poor understanding of many key theoreti al aspe ts of this algorithm, in luding models of the high-level run-time dynami s and identi- ation of those sear h spa e features that in uen e problem diÆ ulty. We onsider these questions in... More
Category : Artificial Intelligence