Recently, a heuristic was proposed for constructing Bayesian networks (BNs) from structured arguments. This heuristic helps domain experts who are accustomed to argumentation to transform their reasoning into a BN and subsequently weigh their case evidence in a probabilistic manner. While the underlying undirected graph of the BN is automatically constructed by following the heuristic, the arc directions are to be set manually by a BN engineer in consultation with the domain expert. As the knowledge elicitation involved is known to be time-consuming, it is of value to (partly) automate this step. We propose a refinement of the heuristic to this end, which specifies the directions in which arcs are to be set given specific conditions on structured arguments.

In violent crimes, adhesive tapes such as duct tape are often used by perpetrators, for example to tie up a victim. In the forensic examination of such tapes many different types of traces can be found, such as finger marks and human biological traces. These traces are first interpreted at source level. However, even when it is certain that a trace was donated by the suspect this does not necessarily mean that he donated the trace while taping the victim, as he could have, for example, used the tape roll from which the pieces came previous to the crime. Therefore, the trace can also be interpreted at activity level. For this, factors such as transfer, persistence and recovery, as well as the position of the trace as it would have been on the original roll have to be taken into consideration. In this study, we have developed a Bayesian network which can aid the forensic practitioner in his interpretation. From a sensitivity analysis, we have concluded that it would be most desirable to set up further studies to determine the most likely positions of DNA on tape rolls if there has only been innocent contact.

Bayesian networks (BNs) are powerful tools that are wellsuited for reasoning about the uncertain consequences that can be inferred from evidence. Domain experts, however, typically do not have the expertise to construct BNs and instead resort to using other tools such as argument diagrams and mind maps. Recently, we proposed a structured approach to construct a BN graph from arguments annotated with causality information. As argumentative inferences may not be causal, we generalize this approach to include other types of inferences in this paper. Moreover, we prove a number of formal properties of the generalized approach and identify assumptions under which the construction of an initial BN graph can be fully automated.

No abstract

In this paper, we propose an argumentation formalism that allows for both deductive and abductive argumentation, where ‘deduction’ is used as an umbrella term for both defeasible and strict ‘forward’ inference. Our formalism is based on an extended version of our previously proposed information graph (IG) formalism, which provides a precise account of the interplay between deductive and abductive inference and causal and evidential information. In the current version, we consider additional types of information such as abstractions which allow domain experts to be more expressive in stating their knowledge, where we identify and impose constraints on the types of inferences that may be performed with the different types of information. A new notion of attack is defined that captures a crucial aspect of abductive reasoning, namely that of competition between abductively inferred alternative explanations. Our argumentation formalism generates an abstract argumentation framework and thus allows arguments to be formally evaluated. We prove that instantiations of our argumentation formalism satisfy key rationality postulates.