Uncertainty in artificial intelligence: Is probability epistemologically and heuristically accurate?
Expert judgment and expert systems
Operations Research
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Fundamental concepts of qualitative probabilistic networks
Artificial Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Qualtitative propagation and scenario-based scheme for exploiting probabilistic reasoning
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
XPLAIN: a system for creating and explaining expert consulting programs
Artificial Intelligence
Efficient reasoning in qualitative probabilistic networks
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
Elicitation of probabilities for belief networks: combining qualitative and quantitative information
UAI'95 Proceedings of the Eleventh conference on Uncertainty in artificial intelligence
Some properties of joint probability distributions
UAI'94 Proceedings of the Tenth international conference on Uncertainty in artificial intelligence
Causality in Bayesian belief networks
UAI'93 Proceedings of the Ninth international conference on Uncertainty in artificial intelligence
Intercausal reasoning with uninstantiated ancestor nodes
UAI'93 Proceedings of the Ninth international conference on Uncertainty in artificial intelligence
| Hi-index | 0.00 |
Although probabilistic knowledge representations and probabilistic reasoning have by now secured their position in artificial intelligence, it is not uncommon to encounter misunderstanding of their foundations and lack of appreciation for their strengths. This paper describes five properties of probabilistic knowledge representations that are particularly useful in intelligent systems research. (1) Directed probabilistic graphs capture essential qualitative properties of a domain, along with its causal structure. (2) Concepts such as relevance and conflicting evidence have a natural, formally sound meaning in probabilistic models. (3) Probabilistic schemes support sound reasoning at a variety of levels ranging from purely quantitative to purely qualitative levels. (4) The role of probability theory in reasoning under uncertainty can be compared to the role of first order logic in reasoning under certainty. Probabilistic knowledge representations provide insight into the foundations of logic-based schemes, showing their difficulties in highly uncertain domains. Finally, (5) probabilistic knowledge representations support automatic generation of understandable explanations of inference for the sake of user interfaces to intelligent systems.