We develop a model of abduction in abstract argumentation, where changes to an argumentation framework act as hypotheses to explain the support of an observation. We present dialogical proof theories for the main decision problems (i.e., finding hypothe- ses that explain skeptical credulous support) and we show that our model can be instantiated on the basis of abductive logic programs.
Goal oriented change of AFs is also studied by Baumann @cite_19 , Baumann and Brewka @cite_0 , @cite_7 and @cite_20 . Furthermore, @cite_10 and Coste- @cite_15 frame it as a problem of . Other studies in which changes to AFs are considered include @cite_18 @cite_12 @cite_16 @cite_17 .
@cite_18 @cite_7 @cite_0 @cite_19 @cite_15 @cite_16 @cite_10 @cite_12 @cite_20 @cite_17
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When a planning task cannot be solved then it can often be made solvable by modifying it a bit: one may change either the set of actions, or the initial state, or the goal description. We show that modification of actions can be reduced to initial state modification. We then apply Katsuno and Mendelzon's distinction between update and revision and show that the modification of the initial state is an update and the modification of the goal description is a revision. We consider variants of Forbus's update and Dalal's revision operation and argue that existing belief change operations do not apply as they stand because their inputs are boolean formulas, while plan task modification involves counterfactual statements. We show that they can be captured in Dynamic Logic of Propositional Assignments DL-PA. We show how belief revision can be treated systematically in the format of dynamicepistemic logic, when operators of conditional belief are added. The core engine consists of definable update rules for changing plausibility relations between worlds, which have been proposed independently in the dynamic-epistemic literature on preference change. Our analysis yields two new types of modal result. First, we obtain complete logics for concrete mechanisms of belief revision, based on compositional reduction axioms. Next, we show how various abstract postulates for belief revision can be analyzed by standard modal frame correspondences for model-changing operations. The process of eliciting client language toward change (change talk [CT]) is implicated as a causal mechanism in motivational interviewing (MI) and brief motivational interventions (BMI). We investigated the articulation of counselor behaviors and CT during BMI with young men. We coded 149 sessions using the Motivational Interviewing Skill Code and summarized these codes into three counselor categories (MI-consistent [MICO], MI-inconsistent [MIIN], other) and three client categories (CT, counter CT [CCT], follow neutral [F N]). We then computed immediate transition frequencies and odds ratios using sequential analysis software. CT was significantly more likely following MICO behaviors, whereas MIIN behaviors only led to CCT and F N. This strongly supports the use of MI skills to elicit CT during BMI with young men, whose speech also predicted counselor behaviors (particularly CT to MICO and CCT to MIIN). Additional analyses showed that among MICO behaviors, reflective listening may be a particularly powerful technique to elicit CT. The paper presents an adaptive approach to perform impact analysis from a given change request to source code. Given a textual change request (e.g., a bug report), a single snapshot (release) of source code, indexed using Latent Semantic Indexing, is used to estimate the impact set. Should additional contextual information be available, the approach configures the best-fit combination to produce an improved impact set. Contextual information includes the execution trace and an initial source code entity verified for change. Combinations of information retrieval, dynamic analysis, and data mining of past source code commits are considered. The research hypothesis is that these combinations help counter the precision or recall deficit of individual techniques and improve the overall accuracy. The tandem operation of the three techniques sets it apart from other related solutions. Automation along with the effective utilization of two key sources of developer knowledge, which are often overlooked in impact analysis at the change request level, is achieved. To validate our approach, we conducted an empirical evaluation on four open source software systems. A benchmark consisting of a number of maintenance issues, such as feature requests and bug fixes, and their associated source code changes was established by manual examination of these systems and their change history. Our results indicate that there are combinations formed from the augmented developer contextual information that show statistically significant improvement over stand-alone approaches.