Asimovian Adaptive Agents

The goal of this research is to develop agents that are adaptive and predictable and timely. At first blush, these three requirements seem contradictory. For example, adaptation risks introducing undesirable side effects, thereby making agents' behavior less predictable. Furthermore, although formal verification can assist in ensuring behavioral predictability, it is known to be time-consuming. Our solution to the challenge of satisfying all three requirements is the following. Agents have finite-state automaton plans, which are adapted online via evolutionary learning (perturbation) operators. To ensure that critical behavioral constraints are always satisfied, agents' plans are first formally verified. They are then reverified after every adaptation. If reverification concludes that constraints are violated, the plans are repaired. The main objective of this paper is to improve the efficiency of reverification after learning, so that agents have a sufficiently rapid response time. We present two solutions: positive results that certain learning operators are a priori guaranteed to preserve useful classes of behavioral assurance constraints (which implies that no reverification is needed for these operators), and efficient incremental reverification algorithms for those learning operators that have negative a priori results

Learning and Verification of Safety Parameters for Airspace Deconfliction

We present a Bayesian approach to learning flexible safety constraints and subsequently verifying whether plans satisfy these constraints. Our approach, called the Safety Constraint Learner/Checker (SCLC), is embedded within the Generalized Integrated Learning Architecture (GILA), which is an integrated, heterogeneous, multi-agent ensemble architecture designed for learning complex problem solving techniques from demonstration by human experts. The SCLC infers safety constraints from a single expert demonstration trace, and applies these constraints to the solutions proposed by the agents in the ensemble. Blame for constraint violations is then transmitted to the individual learning/planning/reasoning agents, thereby facilitating new problem-solving episodes. We discuss the advantages of the SCLC and demonstrate empirical results on an Airspace Planning and Deconfliction Task, which was a benchmark application in the DARPA Integrated Learning Program

Reabilitação de infra-estruturas urbanas de abastecimento de água

Dissertação apresentada para obtenção do grau de Mestre em Engenharia doAmbiente (ramo de Hidráulica e Recursos Hídricos), na Faculdade de Engenharia da Universidade do Porto, sob a orientação dos Professores Doutores J.C. Tentugal Valente e Paulo T. Santos Monteir