A Value-Based Approach for Reasoning with Goal Models

Goal models are used to represent stakeholders’ intentions regarding the system to be developed and the alternative means to achieve those intentions. Goal-oriented analysis techniques have been proposed to help analysts reason when employing goal models. These techniques can be used to identify conflicts among goals, to choose between alternatives or to check the satisfiability of the model. Unfortunately, most of these techniques consider that stakeholders their intentions are equally important. This paper presents a value-based approach that and allows stakeholders to assign a relative importance to the elements in a goal model. It then propagates that importance by means of their relationships (dependencies, contributions and decompositions) in order to determine which elements are more valuable. Fisher’s weighted distribution and multi-criteria decision analysis techniques are used to deal with the propagation of the importance among the goals. The contribution is the alignment of information system with stakeholders and organizational goals

Partial Orderings as Heuristic for Multi-Objective Model-Based Reasoning

Model-based reasoning is becoming increasingly common in software engineering. The process of building and analyzing models helps stakeholders to understand the ramifications of their software decisions. But complex models can confuse and overwhelm stakeholders when these models have too many candidate solutions. We argue here that a technique based on partial orderings lets humans find acceptable solutions via a binary chop needing $O(log(N))$ queries (or less). This paper checks the value of this approach via the iSNEAK partial ordering tool. Pre-experimentally, we were concerned that (a)~our automated methods might produce models that were unacceptable to humans; and that (b)~our human-in-the-loop methods might actual overlooking significant optimizations. Hence, we checked the acceptability of the solutions found by iSNEAK via a human-in-the-loop double-blind evaluation study of 20 Brazilian programmers. We also checked if iSNEAK misses significant optimizations (in a corpus of 16 SE models of size ranging up to 1000 attributes by comparing it against two rival technologies (the genetic algorithms preferred by the interactive search-based SE community; and the sequential model optimizers developed by the SE configuration community~\citep{flash_vivek}). iSNEAK 's solutions were found to be human acceptable (and those solutions took far less time to generate, with far fewer questions to any stakeholder). Significantly, our methods work well even for multi-objective models with competing goals (in this work we explore models with four to five goals). These results motivate more work on partial ordering for many-goal model-based problems

Investigational Paradigms in Downscoring and Upscoring DCIS: Surgical Management Review

Counseling patients with DCIS in a rational manner can be extremely difficult when the range of treatment criteria results in diverse and confusing clinical recommendations. Surgeons need tools that quantify measurable prognostic factors to be used in conjunction with clinical experience for the complex decision-making process. Combination of statistically significant tumor recurrence predictors and lesion parameters obtained after initial excision suggests that patients with DCIS can be stratified into specific subsets allowing a scientifically based discussion. The goal is to choose the treatment regimen that will significantly benefit each patient group without subjecting the patients to unnecessary risks. Exploring the effectiveness of complete excision may offer a starting place in a new way of reasoning and conceiving surgical modalities in terms of “downscoring” or “upscoring” patient risk, perhaps changing clinical approach. Reexcison may lower the specific subsets' score and improve local recurrence-free survival also by revealing a larger tumor size, a higher nuclear grade, or an involved margin and so suggesting the best management. It seems, that the key could be identifying significant relapse predictive factors, according to validated risk investigation models, whose value is modifiable by the surgical approach which avails of different diagnostic and therapeutic potentials to be optimal. Certainly DCIS clinical question cannot have a single curative mode due to heterogeneity of pathological lesions and histologic classification

REBA: A Refinement-Based Architecture for Knowledge Representation and Reasoning in Robotics

This paper describes an architecture for robots that combines the complementary strengths of probabilistic graphical models and declarative programming to represent and reason with logic-based and probabilistic descriptions of uncertainty and domain knowledge. An action language is extended to support non-boolean fluents and non-deterministic causal laws. This action language is used to describe tightly-coupled transition diagrams at two levels of granularity, with a fine-resolution transition diagram defined as a refinement of a coarse-resolution transition diagram of the domain. The coarse-resolution system description, and a history that includes (prioritized) defaults, are translated into an Answer Set Prolog (ASP) program. For any given goal, inference in the ASP program provides a plan of abstract actions. To implement each such abstract action, the robot automatically zooms to the part of the fine-resolution transition diagram relevant to this action. A probabilistic representation of the uncertainty in sensing and actuation is then included in this zoomed fine-resolution system description, and used to construct a partially observable Markov decision process (POMDP). The policy obtained by solving the POMDP is invoked repeatedly to implement the abstract action as a sequence of concrete actions, with the corresponding observations being recorded in the coarse-resolution history and used for subsequent reasoning. The architecture is evaluated in simulation and on a mobile robot moving objects in an indoor domain, to show that it supports reasoning with violation of defaults, noisy observations and unreliable actions, in complex domains.Comment: 72 pages, 14 figure

Narrative based Postdictive Reasoning for Cognitive Robotics

Making sense of incomplete and conflicting narrative knowledge in the presence of abnormalities, unobservable processes, and other real world considerations is a challenge and crucial requirement for cognitive robotics systems. An added challenge, even when suitably specialised action languages and reasoning systems exist, is practical integration and application within large-scale robot control frameworks. In the backdrop of an autonomous wheelchair robot control task, we report on application-driven work to realise postdiction triggered abnormality detection and re-planning for real-time robot control: (a) Narrative-based knowledge about the environment is obtained via a larger smart environment framework; and (b) abnormalities are postdicted from stable-models of an answer-set program corresponding to the robot's epistemic model. The overall reasoning is performed in the context of an approximate epistemic action theory based planner implemented via a translation to answer-set programming.Comment: Commonsense Reasoning Symposium, Ayia Napa, Cyprus, 201