如何使用ofo共享单车评价Probabilistic Models of Cognition

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9.97Carnegie Mellon University30.45Carnegie Mellon University+ 235.58Carnegie Mellon University2.08IBMShow
more authorsAbstractWe introduce an experiment designed to study trade-offs in collaborative decision making environments such as finding the best level of selectivity and abstraction in sharing information, and their impact on the time course and accuracy of group decisions. Two models of the experiment are presented: a cognitive model using the ACT-R cognitive architecture and a probabilistic argumentation model using Markov Random Fields (MARF). The cognitive model relies on memory mechanisms such as spreading activation, partial matching and blending to judge when to share information, which facts are relevant to a given question, and how to aggregate probabilistic evidence. MARF carries out real world reasoning after formal theory of human argumentation while at the same time being flexible to accommodate the deviations from the theory. MARF follows knowledge engineering paradigm aiming at reaching correct reasoning as much as possible. Representative results from the experiment are presented and compared to the results of the two models. Implications of the results and avenues for future work are discussed.Do you want to read the rest of this conference paper?
CitationsCitations0ReferencesReferences0Project[...]The International Technology Alliance in Network and Information Sciences
(NIS-ITA) refers to a series of research programs that were jointly sponsored by UK Ministry of Defence (United Kingdom) a…& ProjectProject[...]Project[...]I am grouping the fantastic RL-related work my collaborators and I did for the IARPA ICArUS project.
The project ended but hopefully the tools and techniques we developed will continue in other fo…& Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.This publication is from a journal that may support self archiving.This Special Issue surveys the state of the art of probabilistic models across a broad range of topics in cognitive science. We suggest that the present shift towards probabilistic methods has deeper origins: viz., conceptual and technical developments in probability theory and statistics that provide the machinery to engage with cognitively relevant information-processing problems. These technical developments provide a rich range of models, tools and metaphors with which to reconceptualize cognition. Moreover, the application of these probabilistic ideas to relevant engineering problems, in speech and image processing, expert systems, robotics and machine learning, has provided a rich source of insights into some of the probabilistic reasoning problems solved by the brain. Here, we highlight some of the key
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