Biblio

Self-adaptive systems commonly operate in heterogeneous contexts and need to consider multiple quality attributes. Human stakeholders often express their quality preferences by defining utility functions, which are used by self-adaptive systems to automatically generate adaptation plans. However, the adaptation space of realistic systems is large and it is obscure how utility functions impact the generated adaptation behavior, as well as structural, behavioral, and quality constraints. Moreover, human stakeholders are often not aware of the underlying tradeoffs between quality attributes. To address this issue, we present an approach that uses machine learning techniques (dimensionality reduction, clustering, and decision tree learning) to explain the reasoning behind automated planning. Our approach focuses on the tradeoffs between quality attributes and how the choice of weights in utility functions results in different plans being generated. We help humans understand quality attribute tradeoffs, identify key decisions in adaptation behavior, and explore how differences in utility functions result in different adaptation alternatives. We present two systems to demonstrate the approach’s applicability and consider its potential application to 24 exemplar self-adaptive systems. Moreover, we describe our assessment of the tradeoff between the information reduction and the amount of explained variance retained by the results obtained with our approach.

Explainability refers to the degree to which a software system’s actions or solutions can be understood by humans. Giving humans the right amount of explanation at the right time is an important factor in maximizing the effective collaboration between an adaptive system and humans during interaction. However, explanations come with costs, such as the required time of explanation and humans’ response time. Hence it is not always clear whether explanations will improve overall system utility and, if so, how the system should effectively provide explanation to humans, particularly given that different humans may benefit from different amounts and frequency of explanation. To provide a partial basis for making such decisions, this paper defines a formal framework that incorporates human personality traits as one of the important elements in guiding automated decision- making about the proper amount of explanation that should be given to the human to improve the overall system utility. Specifically, we use probabilistic model analysis to determine how to utilize explanations in an effective way. To illustrate our approach, Grid – a virtual human and system interaction game -- is developed to represent scenarios for human-systems collaboration and to demonstrate how a human’s personality traits can be used as a factor to consider for systems in providing appropriate explanations.

Many self-adaptive systems benefit from human involvement and oversight, where a human operator can provide expertise not available to the system and can detect problems that the system is unaware of. One way of achieving this is by placing the human operator on the loop – i.e., providing supervisory oversight and intervening in the case of questionable adaptation decisions. To make such interaction effective, explanation is sometimes helpful to allow the human to understand why the system is making certain decisions and calibrate confidence from the human perspective. However, explanations come with costs in terms of delayed actions and the possibility that a human may make a bad judgement. Hence, it is not always obvious whether explanations will improve overall utility and, if so, what kinds of explanation to provide to the operator. In this work, we define a formal framework for reasoning about explanations of adaptive system behaviors and the conditions under which they are warranted. Specifically, we characterize explanations in terms of explanation content, effect, and cost. We then present a dynamic adaptation approach that leverages a probabilistic reasoning technique to determine when the explanation should be used in order to improve overall system utility.

Use of multi-objective probabilistic planning to synthesize behavior of CPSs can play an important role in engineering systems that must self-optimize for multiple quality objectives and operate under uncertainty. However, the reasoning behind automated planning is opaque to end-users. They may not understand why a particular behavior is generated, and therefore not be able to calibrate their confidence in the systems working properly. To address this problem, we propose a method to automatically generate verbal explanation of multi-objective probabilistic planning, that explains why a particular behavior is generated on the basis of the optimization objectives. Our explanation method involves describing objective values of a generated behavior and explaining any tradeoff made to reconcile competing objectives. We contribute: (i) an explainable planning representation that facilitates explanation generation, and (ii) an algorithm for generating contrastive justification as explanation for why a generated behavior is best with respect to the planning objectives. We demonstrate our approach on a mobile robot case study.