Introspection on control-grounded capabilities. An agent inspired approach for control

Introspecció sobre la dinàmica dels agents té un important impacte en decisions individuals i cooperatives en entorns multi-agent. Introspecció, una habilitat cognitiva provinent de la metàfora "agent", permet que els agents siguin conscients de les seves capacitats per a realitzar correctament les tasques. Aquesta introspecció, principalment sobre capacitats relacionades amb la dinàmica, proporciona als agents un raonament adequat per a assolir compromisos segurs en sistemes cooperatius. Per a tal fi, les capacitats garanteixen una representació adequada i explícita de tal dinàmica. Aquest enfocament canvia i millora la manera com els agents poden coordinar-se per a portar a terme tasques i com gestionar les seves interaccions i compromisos en entorns cooperatius. L'enfocament s'ha comprovat en escenaris on la coordinació és important, beneficiosa i necessària. Els resultats i les conclusions són presentats ressaltant els avantatges de la introspecció en la millora del rendiment dels sistemes multi-agent en tasques coordinades i assignació de tasques.

Multivariate Statistical Process Control and Case-Based Reasoning for situation assessment of Sequencing Batch Reactors

ABSRACT This thesis focuses on the monitoring, fault detection and diagnosis of Wastewater Treatment Plants (WWTP), which are important fields of research for a wide range of engineering disciplines. The main objective is to evaluate and apply a novel artificial intelligent methodology based on situation assessment for monitoring and diagnosis of Sequencing Batch Reactor (SBR) operation. To this end, Multivariate Statistical Process Control (MSPC) in combination with Case-Based Reasoning (CBR) methodology was developed, which was evaluated on three different SBR (pilot and lab-scales) plants and validated on BSM1 plant layout.

Prediction of postprandial blood glucose under intra-patient variability and uncertainty and its use in the design of insulin dosing strategies for type 1 diabetic patients

In this thesis I propose a novel method to estimate the dose and injection-to-meal time for low-risk intensive insulin therapy. This dosage-aid system uses an optimization algorithm to determine the insulin dose and injection-to-meal time that minimizes the risk of postprandial hyper- and hypoglycaemia in type 1 diabetic patients. To this end, the algorithm applies a methodology that quantifies the risk of experiencing different grades of hypo- or hyperglycaemia in the postprandial state induced by insulin therapy according to an individual patient’s parameters. This methodology is based on modal interval analysis (MIA). Applying MIA, the postprandial glucose level is predicted with consideration of intra-patient variability and other sources of uncertainty. A worst-case approach is then used to calculate the risk index. In this way, a safer prediction of possible hyper- and hypoglycaemic episodes induced by the insulin therapy tested can be calculated in terms of these uncertainties.