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 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. (shrink)

In a criminal trial, evidence is used to draw conclusions about what happened concerning a supposed crime. Traditionally, the three main approaches to modeling reasoning with evidence are argumentative, narrative and probabilistic approaches. Integrating these three approaches could arguably enhance the communication between an expert and a judge or jury. In previous work, techniques were proposed to represent narratives in a Bayesian network and to use narratives as a basis for systematizing the construction of a Bayesian network for a legal (...) case. In this paper, these techniques are combined to form a design method for constructing a Bayesian network based on narratives. This design method is evaluated by means of an extensive case study concerning the notorious Dutch case of the Anjum murders. (shrink)

It is suggested that taking into account considerations that traditionally fall within the scope of computer science in general, and artificial intelligence in particular, sheds new light on the subject of causation. It is argued that adopting causal notions con be viewed as filling a computational need: They allow reasoning with incomplete information, facilitate economical representations, and afford relatively efficient methods for reasoning about those representations. Specifically, it is proposed that causal reasoning is intimately bound to nonmonotonic reasoning. An account (...) of causation is offered that relies upon this connection, and compares this proposal to previous accounts within philosophy and artificial intelligence. (shrink)

This paper presents a formalism that combines useful properties of both logic and probabilities. Like logic, the formalism admits qualitative sentences and provides symbolic machinery for deriving deductively closed beliefs and, like probability, it permits us to express if-then rules with different levels of firmness and to retract beliefs in response to changing observations. Rules are interpreted as order-of-magnitude approximations of conditional probabilities which impose constraints over the rankings of worlds. Inferences are supported by a unique priority ordering on rules (...) which is syntactically derived from the knowledge base. This ordering accounts for rule interactions, respects specificity considerations and facilitates the construction of coherent states of beliefs. Practical algorithms are developed and analyzed for testing consistency, computing rule ordering, and answering queries. Imprecise observations are incorporated using qualitative versions of Jeffrey's rule and Bayesian updating, with the result that coherent belief revision is embodied naturally and tractably. Finally, causal rules are interpreted as imposing Markovian conditions that further constrain world rankings to reflect the modularity of causal organizations. These constraints are shown to facilitate reasoning about causal projections, explanations, actions and change. (shrink)

Commonsense ontology often conflicts with the ontology of our best scientific and philosophical theories. However, commonsense ontology, and commonsense belief systems in general, seems to be remarkably efficient and cognitively fundamental. In cases of contrast, it is better to find a way to reconcile commonsense and ”theoretical” ontologies. Given that commonsense ontologies are typically expressed within natural language, a classical procedure of reconciliation is semantical. The strategy is that of individuating the ”ontologically problematic” expressions of natural language and paraphrasing the (...) sentences in which they appear in a (formal) language whose commitments are compatible with those of our best theories. We believe that this strategy of reconciliation, though quite standard, especially in the philosophical literature, is problematic: for a start, it forces us to conclude that the ”real content” of our commonsense expressions and beliefs is different from what it appears. Commonsense ontology becomes just an illusion. We will thus propose an alternative approach: according to our view, a commonsense ontology is reconciled with a theoretical ontology in case it is shown that the explanation of why we believe in the existence of a problematic entity is compatible with our best theories. We will call this kind of reconciliation ”epistemic”. The advantage of an epistemic reconciliation is that commonsense ontology is treated in its own right and could be taken prima facie. Another advantage of the view is that epistemic reconciliation can be analysed through the notion of explaining away: a commonsense ontology is epistemically reconciled with a theoretical ontology if and only if the problematic entities of the commonsense ontology are explained away by ”respectable” entities of the theoretical ontology. In the final part of the paper, we sketch a formal analysis of explaining away. (shrink)

I will try to do three things in this paper. First, I want to situate certain problems in natural language semantics with respect to larger trends in logicism, including: (i) Attempts by positivist philosophers earlier in this century to provide a logical basis for the physical sciences; (ii) Attempts by linguists and logicians to develop a “natural language ontology” (and, presumably, a logical language that is related to this ontology by formally explicit rules) that would serve as a framework for (...) natural language semantics. (shrink)