An overview of algorithmic approaches to compute optimum entropy distributions in the expert system shell MECore (extended version)

The expert system shell MECore provides a series of knowledge management operations to define probabilistic knowledge bases and to reason under uncertainty. To provide a reference work for MECore algorithmics, we bring together results from different sources that have been applied in MECore and explain their intuitive ideas. Additionally, we report on our ongoing work regarding further development of MECore's algorithms to compute optimum entropy distributions and provide some empirical results. Altogether this paper explains the intuition of important theoretical results and their practical implications, compares old and new algorithmic approaches and points out their benefits as well as possible limitations and pitfalls.     

Extending adaptive world modeling by identifying and handling insufficient knowledge models

Adaptive knowledge modeling is an approach for extending the abilities of the Object-Oriented World Model, a system for representing the state of an observed real-world environment, to open-world modeling. In open environments, entities unforeseen at the design-time of a world model can occur. For coping with such circumstances, adaptive knowledge modeling is tasked with adapting the underlying knowledge model according to the environment. The approach is based on quantitative measures, introduced previously, for rating the quality of knowledge models. In this contribution, adaptive knowledge modeling is extended by measures for detecting the need for model adaptation and identifying the potential starting points of necessary model change as well as by an approach for applying such change. Being an extended and more detailed version of [17], the contribution also provides background information on the architecture of the Object-Oriented World Model and on the principles of adaptive knowledge modeling, as well as examination results for the proposed methods. In addition, a more complex scenario is used to evaluate the overall approach.     

The Role of Reasoning and Pragmatics in the Modifier Effect

The modifier effect refers to the fact that the perceived likelihood of a property in a noun category is diminished if the noun is modified. For example, “Pigs live on farms” is rated as more likely than “Dirty pigs live on farms.” The modifier effect has been demonstrated in many studies, but the underlying cognitive mechanisms are still unclear. This paper reports two series of experiments that jointly point to the conclusion that the modifier effect is the result of a superposition of three cognitive mechanisms: (a) probabilistically softened default inheritance, (b) pragmatic implicatures due to Gricean relevance assumptions, and (c) inference based on world knowledge. We present evidence that the pragmatic effects operate largely on an unconscious level and that their influence can be reduced by changing the presentation format of the cognitive task.     

PROBabilities from EXemplars (PROBEX): a “lazy” algorithm for probabilistic inference from generic knowledge

PROBEX (PROBabilities from EXemplars), a model of probabilistic inference and probability judgment based on generic knowledge is presented. Its properties are that: (a) it provides an exemplar model satisfying bounded rationality; (b) it is a “lazy” algorithm that presumes no pre‐computed abstractions; (c) it implements a hybrid‐representation, similarity‐graded probability. We investigate the ecological rationality of PROBEX and find that it compares favorably with Take‐The‐Best and multiple regression (Gigerenzer, Todd, & the ABC Research Group, 1999). PROBEX is fitted to the point estimates, decisions, and probability assessments by human participants. The best fit is obtained for a version that weights frequency heavily and retrieves only two exemplars. It is proposed that PROBEX implements speed and frugality in a psychologically plausible way.     

Intelligence and Extraversion in the neural evaluation of delayed rewards

Temporal discounting (TD), the preference for earlier, smaller rewards over delayed, larger rewards, is a pervasive phenomenon that covaries with Big Five personality traits and Intelligence (IQ). This study provides novel insight by identifying correlates for IQ and Extraversion in the neural representation of TD preferences. An intertemporal choice task was employed, where offers were sequentially presented, distinguishing between one evaluation phase (first offer is presented) and one comparison phase (second offer is presented and values are compared). IQ correlated with responses of caudate nucleus to the subjective values of the offers, suggesting a role of cognitive abilities in modulating reward responses. Extraversion correlated with the strength of functional connectivity of a reward evaluation network centered on ventromedial prefrontal cortex.