Improving Adherence to Heart Failure Management Guidelines via Abductive Reasoning

Management of chronic diseases such as heart failure (HF) is a major public health problem. A standard approach to managing chronic diseases by medical community is to have a committee of experts develop guidelines that all physicians should follow. Due to their complexity, these guidelines are difficult to implement and are adopted slowly by the medical community at large. We have developed a physician advisory system that codes the entire set of clinical practice guidelines for managing HF using answer set programming(ASP). In this paper we show how abductive reasoning can be deployed to find missing symptoms and conditions that the patient must exhibit in order for a treatment prescribed by a physician to work effectively. Thus, if a physician does not make an appropriate recommendation or makes a non-adherent recommendation, our system will advise the physician about symptoms and conditions that must be in effect for that recommendation to apply. It is under consideration for acceptance in TPLP.Comment: Paper presented at the 33nd International Conference on Logic Programming (ICLP 2017), Melbourne, Australia, August 28 to September 1, 2017 15 pages, LaTe

Propositional Abduction with Implicit Hitting Sets

Logic-based abduction finds important applications in artificial intelligence and related areas. One application example is in finding explanations for observed phenomena. Propositional abduction is a restriction of abduction to the propositional domain, and complexity-wise is in the second level of the polynomial hierarchy. Recent work has shown that exploiting implicit hitting sets and propositional satisfiability (SAT) solvers provides an efficient approach for propositional abduction. This paper investigates this earlier work and proposes a number of algorithmic improvements. These improvements are shown to yield exponential reductions in the number of SAT solver calls. More importantly, the experimental results show significant performance improvements compared to the the best approaches for propositional abduction

Backdoors to Abduction

Abductive reasoning (or Abduction, for short) is among the most fundamental AI reasoning methods, with a broad range of applications, including fault diagnosis, belief revision, and automated planning. Unfortunately, Abduction is of high computational complexity; even propositional Abduction is \Sigma_2^P-complete and thus harder than NP and coNP. This complexity barrier rules out the existence of a polynomial transformation to propositional satisfiability (SAT). In this work we use structural properties of the Abduction instance to break this complexity barrier. We utilize the problem structure in terms of small backdoor sets. We present fixed-parameter tractable transformations from Abduction to SAT, which make the power of today's SAT solvers available to Abduction.Comment: 12 pages, a short version will appear in the proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI 2013

Expressiveness of Communication in Answer Set Programming

Answer set programming (ASP) is a form of declarative programming that allows to succinctly formulate and efficiently solve complex problems. An intuitive extension of this formalism is communicating ASP, in which multiple ASP programs collaborate to solve the problem at hand. However, the expressiveness of communicating ASP has not been thoroughly studied. In this paper, we present a systematic study of the additional expressiveness offered by allowing ASP programs to communicate. First, we consider a simple form of communication where programs are only allowed to ask questions to each other. For the most part, we deliberately only consider simple programs, i.e. programs for which computing the answer sets is in P. We find that the problem of deciding whether a literal is in some answer set of a communicating ASP program using simple communication is NP-hard. In other words: we move up a step in the polynomial hierarchy due to the ability of these simple ASP programs to communicate and collaborate. Second, we modify the communication mechanism to also allow us to focus on a sequence of communicating programs, where each program in the sequence may successively remove some of the remaining models. This mimics a network of leaders, where the first leader has the first say and may remove models that he or she finds unsatisfactory. Using this particular communication mechanism allows us to capture the entire polynomial hierarchy. This means, in particular, that communicating ASP could be used to solve problems that are above the second level of the polynomial hierarchy, such as some forms of abductive reasoning as well as PSPACE-complete problems such as STRIPS planning.Comment: 35 pages. This article has been accepted for publication in Theory and Practice of Logic Programming, Copyright Cambridge University Pres

Abductive Reasoning and Automated Analysis of Feature Models: How are they connected?

In the automated analysis feature models (AAFM), many operations have been defined to extract relevant information to be used on decision making. Most of the proposals rely on logics to give solution to different operations. This extraction of knowledge using logics is known as deductive reasoning. One of the most useful operations are explanations that provide the reasons why some other operations find no solution. However, explanations does not use deductive but abductive reasoning, a kind of reasoning that allows to obtain conjectures why things happen. As a first contribution we differentiate between deductive and abductive reasoning and show how this difference affect to AAFM. Secondly, we broaden the concept of explanations relying on abductive reasoning, applying them even when we obtain a positive response from other operations. Lastly, we propose a catalog of operations that use abduction to provide useful information.Comisión Interministerial de Ciencia y Tecnología (CICYT) TIN2006-00472Junta de Andalucía TIC-253

On the Relationship Between KR Approaches for Explainable Planning

In this paper, we build upon notions from knowledge representation and reasoning (KR) to expand a preliminary logic-based framework that characterizes the model reconciliation problem for explainable planning. We also provide a detailed exposition on the relationship between similar KR techniques, such as abductive explanations and belief change, and their applicability to explainable planning

Transformation As Search

In model-driven engineering, model transformations are con- sidered a key element to generate and maintain consistency between re- lated models. Rule-based approaches have become a mature technology and are widely used in different application domains. However, in var- ious scenarios, these solutions still suffer from a number of limitations that stem from their injective and deterministic nature. This article pro- poses an original approach, based on non-deterministic constraint-based search engines, to define and execute bidirectional model transforma- tions and synchronizations from single specifications. Since these solely rely on basic existing modeling concepts, it does not require the intro- duction of a dedicated language. We first describe and formally define this model operation, called transformation as search, then describe a proof-of-concept implementation and discuss experiments on a reference use case in software engineering

Secondary predication in Russian

The paper makes two contributions to semantic typology of secondary predicates. It provides an explanation of the fact that Russian has no resultative secondary predicates, relating this explanation to the interpretation of secondary predicates in English. And it relates depictive secondary predicates in Russian, which usually occur in the instrumental case, to other uses of the instrumental case in Russian, establishing here, too, a difference to English concerning the scope of the secondary predication phenomenon

On Logic-Based Explainability with Partially Specified Inputs

In the practical deployment of machine learning (ML) models, missing data represents a recurring challenge. Missing data is often addressed when training ML models. But missing data also needs to be addressed when deciding predictions and when explaining those predictions. Missing data represents an opportunity to partially specify the inputs of the prediction to be explained. This paper studies the computation of logic-based explanations in the presence of partially specified inputs. The paper shows that most of the algorithms proposed in recent years for computing logic-based explanations can be generalized for computing explanations given the partially specified inputs. One related result is that the complexity of computing logic-based explanations remains unchanged. A similar result is proved in the case of logic-based explainability subject to input constraints. Furthermore, the proposed solution for computing explanations given partially specified inputs is applied to classifiers obtained from well-known public datasets, thereby illustrating a number of novel explainability use cases.Comment: 14 page

Engineering Adaptive Digital Investigations using Forensics Requirements

A digital forensic investigation aims to collect and analyse the evidence necessary to demonstrate a potential hypothesis of a digital crime. Despite the availability of several digital forensics tools, investigators still approach each crime case from scratch, postulating potential hypotheses and analysing large volumes of data. This paper proposes to explicitly model forensic requirements in order to engineer software systems that are forensic-ready and guide the activities of a digital investigation. Forensic requirements relate some speculative hypotheses of a crime to the evidence that should be collected and analysed in a crime scene. In contrast to existing approaches, we propose to perform proactive activities to preserve important - potentially ephemeral - evidence, depending on the risk of a crime to take place. Once an investigation starts, the evidence collected proactively is analysed to assess if some of the speculative hypotheses of a crime hold and what further evidence is necessary to support them. For each hypothesis that is satisfied, a structured argument is generated to demonstrate how the evidence collected supports that hypothesis. Our evaluation results suggest that the approach provides correct investigative findings and reduces significantly the amount of evidence to be collected and the hypotheses to be analysed