Probabilistic estimation of uncertain temporal relations

A wide range of AI applications should manage time varying information, for example, temporal databases, reservation systems, keeping medical records, financial applications, planning. Many published research articles in the area of temporal representation and reasoning assume that temporal data is precise and certain, even though in reality this assumption is often false. In many situations there is a need to know the relation between two temporal intervals, as it is, for example, during query processing. Indeterminacy means that we do not know exactly when a particular event happened. When two temporal intervals are indeterminate it is in many cases impossible to derive a certain temporal relation between them.Keywords: Uncertain temporal relation, point, interval, probability

Virtual integration of temporal and conflicting information

This paper is presenting a way of integrating conflicting temporal information from multiple information providers considering a property-based resolution. The properties considered in this paper are the time and uncertainty because of conflicting information providers. The property based resolution requires a flexible query mechanism, where answers are considered as bounds, taking into account the tendency of things to occur and also the might happen ability of things. Finally some attention is paid to a database environment with non-static members

Comment on Identification with Taylor Rules: is it indeed impossible? Extended version

Cochrane (2007) points out that the Taylor rule parameters in New-Keynesian models are not identified, and thus trying to estimate them through single-equation regressions is pointless. This paper shows in contrast that this observation holds only for economies that do not display inflation inertia or habit formation. These inherent features of aggregate data allow to correctly identify the parameters of the monetary policy rule by single-equation analysis.monetary economics ;

A NEW LOOK AT THE TRADE VOLUME EFFECTS OF REAL EXCHANGE RATE RISK

This paper takes a new empirical look at the longstanding question of the effect of exchange rate volatility on international trade flows by studying the case of Taiwan's exports to the United States from 1989-1998. In particular, we employ sectoral level, monthly data and a multivariate GARCH-M estimator with corrections for leptokurtic errors that is consistent with the core hypothesis that traders' forward contracting behavior might be affected by exchange rate risk. We find that real exchange rate risk has insignificant effects in most sectors, although agricultural trade volumes appear highly responsive to real exchange rate volatility. These results differ significantly from those obtained using more conventional and restrictive modeling assumptions.International Relations/Trade, F1, F3,

Fuzzy Cognitive Maps and Neutrosophic Cognitive Maps

As extension of Fuzzy Cognitive Maps are now introduced the Neutrosophic Cognitive Map

A survey of qualitative spatial representations

Representation and reasoning with qualitative spatial relations is an important problem in artificial intelligence and has wide applications in the fields of geographic information system, computer vision, autonomous robot navigation, natural language understanding, spatial databases and so on. The reasons for this interest in using qualitative spatial relations include cognitive comprehensibility, efficiency and computational facility. This paper summarizes progress in qualitative spatial representation by describing key calculi representing different types of spatial relationships. The paper concludes with a discussion of current research and glimpse of future work