Since Keynes no economist would deny that expectations under uncertain conditions matter for the conduct of monetary policy, but still opinions about their formation are diverse. We build a hybrid New Keynesian Framework to analyze the in‡uence of model uncertainty on optimal interest rates under di¤erent degrees of rational forward-looking behavior, using recently developed robust control tech...

Observation uncertainty techniques offer a way to dynamically compensate automatic speech recognizers to account for the information missing in real world scenarios. These techniques have been demonstrated to effectively be able to compensate multiple environment distortions and improve the integration of ASR systems with speech enhancement pre-processing through uncertainty propagation. Unfort...

We present a simulation-based verification framework for hybrid systems modeled by a subclass of non-linear hybrid automata with inputs. In general, the motivation for using simulation in verification is to obtain information of the system behavior without finding the (usually non-existing or costly-to-get) analytical solution of the system dynamics. The initial state and input set are partitio...

Uncertainty analysis of future system operation is the key feature of current study. This paper shows that how subjective differences in representing fuzzy inputs can affect the final outputs. We consider a confined range of forecasted samples in terms of fuzzy membership functions as well as the range of historical forecasting errors in terms of probability density functions. On this basis, th...

In this paper, we investigate nonlinear reachability computation in presence of model uncertainty, via guaranteed set integration. We show how this can be done by using the classical Müller’s existence theorem. The core idea developed is to no longer deal with whole sets but to derive instead two nonlinear dynamical systems which involve no model uncertainty and which bracket in a guaranteed wa...

This paper aims at bridging together the fields of Uncertain Reasoning and Knowledge Representation. The bridge we propose consists in the definition of a Hybrid Belief System, a general modular system capable of performing uncertain reasoning on structured knowledge. This system comprises two distinct modules, UR-mod and KR-mod: the UR-mod provides the uncertainty calculus used to represent un...

In this paper we discuss different architectures for reasoning under uncertainty related to our ongoing research into building a medical decision support system. The uncertainty in the medical domain can be divided into a well understood part and a less understood part. This motivates the use of a hybrid decision support system, and in particular, we argue that a Bayesian network should be used...

Artificial intelligence is an emerging topic and will soon be able to perform decisions better than humans. In more complex and creative contexts such as innovation, however, the question remains whether machines are superior to humans. Machines fail in two kinds of situations: processing and interpreting “soft” information (information that cannot be quantified) and making predictions in “unkn...

This paper is on Fuzzy Stochastic Optimisation, an area that is quickly coming to the forefront of mathematical programming under uncertainty. An even stronger motivating factor for the growing interest in this area can be found in the ubiquitous nature of decision problems involving hybrid undeterminacy. More precisely, we consider a range of situations in which random factors and fuzzy inform...