Syntactic Reasoning with Conditional Probabilities in Deductive Argumentation

Evidence from studies, such as in science or medicine, often corresponds to conditional probability statements. Furthermore, evidence can conflict, in particular when coming from multiple studies. Whilst it is natural to make sense of such evidence using arguments, there is a lack of a systematic formalism for representing and reasoning with conditional probability statements in computational argumentation. We address this shortcoming by providing a formalization of conditional probabilistic argumentation based on probabilistic conditional logic. We provide a semantics and a collection of comprehensible inference rules that give different insights into evidence. We show how arguments constructed from proofs and attacks between them can be analyzed as arguments graphs using dialectical semantics and via the epistemic approach to probabilistic argumentation. Our approach allows for a transparent and systematic way of handling uncertainty that often arises in evidence

Syntactic reasoning with conditional probabilities in deductive argumentation

Evidence from studies, such as in science or medicine, often corresponds to conditional probability statements. Furthermore, evidence can conflict, in particular when coming from multiple studies. Whilst it is natural to make sense of such evidence using arguments, there is a lack of a systematic formalism for representing and reasoning with conditional probability statements in computational argumentation. We address this shortcoming by providing a formalization of conditional probabilistic argumentation based on probabilistic conditional logic. We provide a semantics and a collection of comprehensible inference rules that give different insights into evidence. We show how arguments constructed from proofs and attacks between them can be analyzed as arguments graphs using dialectical semantics and via the epistemic approach to probabilistic argumentation. Our approach allows for a transparent and systematic way of handling uncertainty that often arises in evidence

Project Risk Management by a Probabilistic Expert System

Abstract Efficient applications of expert systems to project risk management problems are seldom, if not unusual. In this paper we overcome this lack by using the probabilistic expert system shell SPIRIT. The rule-based shell’s power in conditioning, inference and reasoning under incomplete information will work well on risk estimation and classification. A key characteristic of SPIRIT is the possibility to integrate project objectives into the risk management model. So known dependencies between risk variables can be modelled by the user if known beforehand, whereas hidden dependencies might be detected by the proper system. Because of the novelty of projects they suffer from incomplete information and it is this incompleteness which SPIRIT handles at high information fidelity. Furthermore undirected inference is possible, due to the undirected graphical structure in which knowledge is acquired and processed. So, in an early-state risk management situation – where the final model in terms of certain variables and/or their respective dependencies is not yet available – preliminary risk analyses and even recommendations for adequate risk treatment measures are possible, too. A middle size product developement example, including 12 binary variables and 34 rules, shows the inferential power of SPIRIT. 1