Abstract: Advances in CAD and CAM have enabled engineers and design teams to digitally design parts with unprecedented ease. Software solutions now come with a range of modules for optimizing designs for performance requirements, generating instructions for manufacturing, and digitally tracking the entire process from design to procurement in the form of product life-cycle management tools. However, existing solutions force design teams and corporations to take a primarily serial approach where manufacturing and procurement decisions are largely contingent on design, rather than being an integral part of the design process. In this work, we propose a new approach to part making where design, manufacturing, and supply chain requirements and resources can be jointly considered and optimized. We present the Generative Manufacturing compiler that accepts as input the following: 1) An engineering part requirements specification that includes quantities such as loads, domain envelope, mass, and compliance, 2) A business part requirements specification that includes production volume, cost, and lead time, 3) Contextual knowledge about the current manufacturing state such as availability of relevant manufacturing equipment, materials, and workforce, both locally and through the supply chain. Based on these factors, the compiler generates and evaluates manufacturing process alternatives and the optimal derivative designs that are implied by each process, and enables a user guided iterative exploration of the design space. As part of our initial implementation of this compiler, we demonstrate the effectiveness of our approach on examples of a cantilever beam problem and a rocket engine mount problem and showcase its utility in creating and selecting optimal solutions according to the requirements and resources.

Abstract: Building and operating software-intensive systems often involves exploring decision spaces made up of large numbers of variables and complex relations among them. Understanding such spaces is often overwhelming to human decision makers, who have limited capacity to digest large amounts of information, making it difficult to distinguish the forest through the trees. In this article, we report on our experience in which we used dimensionality reduction techniques to enable decision makers in different domains (software architecture, smart manufacturing, automated planning for service robots) to focus on the elements of the decision space that explain most of the quality variation, filtering out noise, and thus reducing cognitive complexity.

Abstract: In software design, guaranteeing the correctness of run-time system behavior while achieving an acceptable balance among multiple quality attributes remains a challenging problem. Moreover, providing guarantees about the satisfaction of those requirements when systems are subject to uncertain environments is even more challenging. While recent developments in architectural analysis techniques can assist architects in exploring the satisfaction of quantitative guarantees across the design space, existing approaches are still limited because they do not explicitly link design decisions to satisfaction of quality requirements. Furthermore, the amount of information they yield can be overwhelming to a human designer, making it difficult to see the forest for the trees. In this paper we present ExTrA (Explaining Tradeoffs of software Architecture design spaces), an approach to analyzing architectural design spaces that addresses these limitations and provides a basis for explaining design tradeoffs. Our approach employs dimensionality reduction techniques employed in machine learning pipelines like Principal Component Analysis (PCA) and Decision Tree Learning (DTL) to enable architects to understand how design decisions contribute to the satisfaction of extra-functional properties across the design space. Our results show feasibility of the approach in two case studies and evidence that combining complementary techniques like PCA and DTL is a viable approach to facilitate comprehension of tradeoffs in poorly-understood design spaces.

Abstract: The emergence of powerful automated design tools in many domains is changing the nature of design, as human-intensive activities can be increasingly off-loaded to those tools. Rather than having a human consider only handful of options, as has been done historically, such tools now enable the generation of a large space of potential designs, exhibiting different tradeoffs among competing qualities of merit, and supporting systematic exploration of the design space. At the same time, this paradigm raises new challenges centered on enabling humans to effectively navigate that generated space in order to select a design that best meets their requirements. In this paper we describe our experience in the domain of generative manufacturing, in which we developed a novel design environment for airplane parts manufacturing that incorporates a number of sophisticated design tools and attempts to tackle the emergent problems of design space exploration that are faced by designers of those parts. We use this experience to highlight the challenges that we faced and reflect on their applicability more generally to tool-assisted software design environments.

Abstract: Over the past decade, a number of automated techniques and tools have been developed for optimizing architectural designs with respect to quality-attribute goals. In these systems, the optimization process is typically seen as a black box, since it is not possible for a human to have access to the decisions that led to a particular solution generated by an optimization tool. Even when these decisions are available for inspection, the amount of information can be overwhelming for the architect. As a result, humans might not completely understand the rationale behind a given solution or trust that a tool made correct decisions. To mitigate this problem, we propose a semi-automated approach for generating textual explanations for any architectural solution produced by a tool. This kind of explanation provides a summary of the key architectural tactics that were applied to achieve an optimized architecture that satisfies a set of quality-attribute objectives. In this paper, we discuss two procedures for determining the key tactics to be explained. As an initial experiment, we used a popular optimization tool to generate solutions and explanations for a small but non-trivial design space involving performance, reliability, and cost objectives. We also performed an exploratory user study to assess the effectiveness of these explanations.

Abstract: In software design, guaranteeing the correctness of run-time system behavior while achieving an acceptable balance among multiple quality attributes remains a challenging problem. Moreover, providing guarantees about the satisfaction of those requirements when systems are subject to uncertain environments is even more challenging. While recent developments in architectural analysis techniques can assist architects in exploring the satisfaction of quantitative guarantees across the design space, existing approaches are still limited because they do not explicitly link design decisions to satisfaction of quality requirements. Furthermore, the amount of information they yield can be overwhelming to a human designer, making it difficult to distinguish the forest through the trees. In this paper, we present an approach to analyzing architectural design spaces that addresses these limitations and provides a basis to enable the explainability of design tradeoffs. Our approach combines dimensionality reduction techniques employed in machine learning pipelines with quantitative verification to enable architects to understand how design decisions contribute to the satisfaction of strict quantitative guarantees under uncertainty across the design space. Our results show feasibility of the approach in two case studies and evidence that dimensionality reduction is a viable approach to facilitate comprehension of tradeoffs in poorly-understood design spaces.

Abstract: Software systems are designed and implemented with assumptions about the environment. However, once the system is deployed,the actual environment may deviate from its expected behavior,possibly undermining desired properties of the system. To enable systematic design of systems that are robust against potential environmental deviations, we propose a rigorous notion of robustness for software systems. In particular, the robustness of a system is de-fined as the largest set of deviating environmental behaviors under which the system is capable of guaranteeing a desired property. We describe a new set of design analysis problems based on our notion of robustness, and a technique for automatically computing robustness of a system given its behavior description. We demonstrate potential applications of our robustness notion on two case studies involving network protocols and safety-critical interfaces.

Abstract: End-users’ trust in automated agents is important as automated decision-making and planning is increasingly used in many aspects of people’s lives. In real-world applications of planning, multiple optimization objectives are often involved.Thus, planning agents’ decisions can involve complex tradeoffs among competing objectives. It can be difficult for the end-users to understand why an agent decides on a particular planning solution on the basis of its objective values. As a result, the users may not know whether the agent is making the right decisions, and may lack trust in it. In this work, we contribute an approach, based on contrastive explanation, that enables a multi-objective MDP planning agent to explain its decisions in a way that communicates its tradeoff rationale in terms of the domain-level concepts. We conduct a human subjects experiment to evaluate the effectiveness of our explanation approach in a mobile robot navigation domain. The results show that our approach significantly improves the users’ understanding, and confidence in their understanding, of the tradeoff rationale of the planning agent

Abstract: 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.

Abstract: As Cyber-Physical Systems (CPSs) become more autonomous, it becomes harder for humans who interact with the CPSs to understand the behavior of the systems. Particularly for CPSs that must perform tasks while optimizing for multiple quality objectives and acting under uncertainty, it can be difficult for humans to understand the system behavior generated by an automated planner. This work-in-progress presents an approach at clarifying system behavior through interactive explanation by allowing end-users to ask Why and Why-Not questions about specific behaviors of the system, and providing answers in the form of contrastive explanation.

Abstract: Sequential decision-making problems with multiple objectives are natural to many application domains of AI-enabled systems. As these systems are increasingly used to work with people or to make decisions that impact people, it is important that their reasoning is intelligible to the end-users and stakeholders, to foster trust and effective human-agent collaborations. However, understanding the reasoning behind solving sequential decision problems is difficult for end-users even when white-box decision models such as Markov decision processes (MDPs) are used. Such intelligibility challenge is due to the combinatorial explosion of possible strategies for solving long-horizon problems. The multi-objective optimization aspect further complicates the problem as different objectives may conflict and reasoning about tradeoffs is required. These complexities pose a barrier for end-users to know whether the agent has made the right decisions for a given context, and may prohibit them from intervening if the agent was wrong. The goal of this thesis is to develop an explainability framework that enables the agent making sequential decisions to communicate its goals and rationale for its behavior to the end-users.

We present an explainable planning framework for MDP, particularly to support problem domains with multiple optimization objectives. We propose consequence-oriented contrastive explanations, in which an argument for an agent's policy is in terms of its expected consequences on the task objectives, put in context of the selected viable alternatives to demonstrate the optimization and tradeoff reasoning of the agent. Our modeling framework supports reward decomposition, and augments MDP representation to ground the components of the reward or cost function in the domain-level concepts and semantics, to facilitate explanation generation. Our explanation generation method computes policy-level contrastive foils that describe the inflection points in the agent's decision making in terms of optimization and trade-off reasoning of the decomposed task objectives. We demonstrate the applicability of our explainable planning framework by applying it to three planning problem domains: waypoint-based navigation, UAV mission planning, and clinic scheduling.

We design and conduct a human subjects experiment to evaluate the effectiveness of explanations based on measurable task performance. We design the users' task in the experiment to be: assessing the agent's planning decisions to determine whether they are the best decisions for a given problem context. Our experimental results show that our proposed consequence-oriented contrastive explanation approach significantly improves the users' ability to correctly assess the agent's planning decisions, as well as the users' confidence in their assessment.

Lastly, we investigate the feasibility of a user-guided approach to our consequence oriented contrastive explanation paradigm. We propose a theoretical framework and approaches to formulate Why Not behavioral questions as state-action constraints and linear temporal logic constraints on the planning problem, and to solve for satisfying policies in order to explain the full impact that the queried behavior has on the subsequent decisions and on the task objectives.

Abstract: Increasingly software systems operate in environments of change and uncertainty, where the system's ability to satisfy its quality objectives depends on its ability to adapt. Self-adaptation allows these systems to manage this challenge by autonomously adapting to changes in their environments. While self-* systems are designed precisely to manage uncertainty, unexpected changes may violate design assumptions, resulting in the system failing to satisfy its quality attribute requirements. When this occurs, the planner must generate a new plan, an expensive operation for large systems. As autonomous systems increase in size, interconnectedness, and complexity, this cost can quickly become prohibitive.

This thesis addresses this problem by leveraging information contained in prior plans to reduce the replanning necessary to respond to an unexpected change. Even in the face of an unexpected change, some of the insights contained in existing plans are likely to remain applicable. For example, an autonomous aerial vehicle encountering an unexpected obstacle will need to replan to avoid the obstacle, but the drone may be able to return to its prior plan after this maneuver. A larger change will reduce the amount of reuse that is possible, for example changing the drone's mission to fly to a new location, but still, the takeoff and landing procedures may be reused. This thesis reuses existing adaptation plans by seeding a genetic algorithm with these plans. This enables a scalable self-* planner that can replan in complex systems with large search spaces.

While the idea of plan reuse is intuitive, in practice plan reuse is difficult and may even be worse than replanning from scratch if not performed carefully. This dissertation provides reuse enhancing approaches to reduce the evaluation time of candidate plans, an approach for building reusable repertoires of plans and identifying generalizable plan fragments, and a co-evolutionary extension to enable plan reuse for security. The thesis is evaluated on three simulated case study systems, including a cloud-based web service provider, a team of autonomous aerial vehicles, and an enterprise business system under a cyber attack. Ultimately, plan reuse will enable large self-* systems to replan even after unexpected changes.