43 research outputs found
Modifications of the Miller definition of contrastive (counterfactual) explanations
Miller recently proposed a definition of contrastive (counterfactual)
explanations based on the well-known Halpern-Pearl (HP) definitions of causes
and (non-contrastive) explanations. Crucially, the Miller definition was based
on the original HP definition of explanations, but this has since been modified
by Halpern; presumably because the original yields counterintuitive results in
many standard examples. More recently Borner has proposed a third definition,
observing that this modified HP definition may also yield counterintuitive
results. In this paper we show that the Miller definition inherits issues found
in the original HP definition. We address these issues by proposing two
improved variants based on the more robust modified HP and Borner definitions.
We analyse our new definitions and show that they retain the spirit of the
Miller definition where all three variants satisfy an alternative unified
definition that is modular with respect to an underlying definition of
non-contrastive explanations. To the best of our knowledge this paper also
provides the first explicit comparison between the original and modified HP
definitions.Comment: Accepted by ECSQARU'2
Measuring inconsistency in a network intrusion detection rule set based on Snort
In this preliminary study, we investigate how inconsistency in a network intrusion detection rule set can be measured. To achieve this, we first examine the structure of these rules which are based on Snort and incorporate regular expression (Regex) pattern matching. We then identify primitive elements in these rules in order to translate the rules into their (equivalent) logical forms and to establish connections between them. Additional rules from background knowledge are also introduced to make the correlations among rules more explicit. We measure the degree of inconsistency in formulae of such a rule set (using the Scoring function, Shapley inconsistency values and Blame measure for prioritized knowledge) and compare the *This is a revised and significantly extended version of [1]
Intention Interleaving Via Classical Replanning
The BDI architecture, where agents are modelled based on their belief, desires, and intentions, provides a practical approach to developing intelligent agents. One of the key features of BDI agents is that they are able to pursue multiple intentions in parallel, i.e. in an interleaved manner. Most of the previous works have enabled BDI agents to avoid negative interactions between intentions to ensure the correct execution. However, to avoid execution inefficiencies, BDI agents should also capitalise on positive interactions between intentions. In this paper, we provide a theoretical framework where first-principles planning (FPP) is employed to manage the intention interleaving in an automated fashion. Our FPP approach not only guarantees the achievability of intentions, but also discovers and exploits potential common sub-intentions to reduce the overall cost of intention execution. Our results show that our approach is both theoretically sound and practically feasible. The effectiveness evaluation in a manufacturing scenario shows that our approach can significantly reduce the total number of actions by merging common sub-intentions, while still accomplishing all intentions