Abstract: Robotic systems are typically composed of various subsystems, such as localization and navigation, each encompassing numerous configurable components (e. g., selecting different planning algorithms). Once an algorithm has been selected for a component, its associated configuration options must be set to the appropriate values. Configuration options across the system stack interact non-trivially. Finding optimal configurations for highly configurable robots to achieve desired performance poses a significant challenge due to the interactions between configuration options across software and hardware that result in an exponentially large and complex configuration space. These challenges are further compounded by the need for transferability between different environments and robotic platforms. Data efficient optimization algorithms (e. g., Bayesian optimization) have been increasingly employed to automate the tuning of configurable parameters in cyber-physical systems. However, such optimization algorithms converge at later stages, often after exhausting the allocated budget (e. g., optimization steps, allotted time) and lacking transferability. This paper proposes CURE - a method that identifies causally relevant configuration options, enabling the optimization process to operate in a reduced search space, thereby enabling faster optimization of robot performance. CURE abstracts the causal relationships between various configuration options and robot performance objectives by learning a causal model in the source (a low-cost environment such as the Gazebo simulator) and applying the learned knowledge to perform optimization in the target (e. g., Turtlebot 3 physical robot). We demonstrate the effectiveness and transferability of CURE by conducting experiments that involve varying degrees of deployment changes in both physical robots and simulation

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: Self-adaptation improves the resilience of software-intensive systems, enabling them to adapt their structure and behavior to run-time changes (e.g., in workload and resource availability). Many of these approaches reason about the best way of adapting by synthesizing adaptation plans online via planning or model checking tools. This method enables the exploration of a rich solution space, but optimal solutions and other guarantees (e.g., constraint satisfaction) are computationally costly, resulting in long planning times during which changes may invalidate plans. An alternative to online planning involves selecting at run time the adaptation best suited to the current system and environment conditions from among a predefined repertoire of adaptation strategies that capture repair and optimization tasks. This method does not incur run-time overhead but requires additional effort from engineers, who have to specify strategies and lack support to systematically assess their quality. In this article, we present MOSAICO, an approach for offline synthesis of adaptation strategy repertoires that makes a novel use of discrete abstractions of the state space to flexibly adapt extra-functional behavior in a scalable manner. The approach supports making trade-offs: (i) among multiple extra-functional concerns, and (ii) between computation time and adaptation quality (varying abstraction resolution). Our results show a remarkable improvement on system qualities in contrast to manually-specified repertoires. More interestingly, moderate increments in abstraction resolution can lead to pronounced quality improvements, whereas high resolutions yield only negligible improvement over medium resolutions.

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: Self-adaptive systems increasingly need to reason about and adapt both structural and behavioral system aspects, such as in mobile service robots, which must reason about missions that they need to achieve and the architecture of the software executing them. Deciding how to best adapt these systems to run time changes is challenging because it entails considering mutual dependencies between the software architecture that the system is running and the outcome of plans for completing tasks, while also considering multiple trade-offs and uncertainties. Considering all these aspects in planning for adaptation often yields large solution spaces which cannot be adequately explored at run time. We address this challenge by proposing a planning approach able to consider the impact of mutual dependencies between software architecture and task planning on the satisfaction of mission goals. The approach is able to reason quantitatively about the outcome of adaptation decisions handling both the reconfiguration of the system's architecture and adaptation of task plans under uncertainty and in a rich trade-off space. Our results show: (i) feasibility of run-time decision-making for self-adaptation in an otherwise intractable solution space by dividing-and-conquering adaptation into architecture reconfiguration and task planning sub-problems, and (ii) improved quality of adaptation decisions with respect to decision making that does not consider dependencies between architecture and task planning.

Abstract: Self-adaptive software systems make decisions at run time that seek to change their behavior in response to faults, changing environments and attacks. Therefore, having an appropriate planning approach to find an adaptation plan is critical to successful self-adaptation. For many realistic systems, ideally one would like to have a planning approach that is both quick and finds an optimal adaptation plan. However, due to the fundamental trade-off between quality and timeliness of planning, often designers have to compromise between an approach that is quick to find a plan and an approach that is slow but finds an optimal plan. To deal with this trade-off, this work proposes a hybrid planning approach that combines more than one planning approach to bring their benefits together.

Abstract: Due to the fundamental trade-off between quality and timeliness of planning, designers of self-adaptive systems often have to compromise between an approach that is quick to find an adaptation plan and an approach that is slow but finds a quality adaptation plan. To deal with this trade-off, in our previous work, we proposed a hybrid planning approach that combines a deliberative and a reactive planning approach to find a balance between quality and timeliness of planning.

However, when reactive and deliberative planning is combined to

instantiate a hybrid planner, the key challenge is to decide which

approach(es) should be invoked to solve a planning problem. To

this end, this paper proposes to use a data-driven instance-based

learning to find an appropriate combination of the two planning

approaches when solving a planning problem. As an initial proof

of concept, the paper presents results of a small experiment that

demonstrates the potential of the proposed approach to identify a

combination of the two planning approaches to solve a planning

problem.

Abstract: Approaches to self-adaptation face a fundamental trade-off between quality and timeliness in decision-making. Due to this trade-off, designers of self-adaptive systems often have to find a fixed and suboptimal compromise between these two requirements. Recent work has proposed the hybrid planning approach that can resolve this trade-off dynamically and potentially in an optimal way. The promise of hybrid planning is to combine multiple planners at run time to produce adaptation plans of the highest quality within given time constraints. However, since decision-making approaches are complex and diverse, the problem of combining them is even more difficult, and no frameworks for hybrid planning. This paper makes an important step in simplifying the problem of hybrid planning by formalizing it and decomposing it into four simpler subproblems. These formalizations will serve as a foundation for creating and evaluating engineering solutions to the hybrid planning problem.

Abstract: Run-time generation of adaptation plans is a powerful mechanism that helps a self-adaptive system to meet its goals in a dynamically changing environment. In the past, researchers have demonstrated successful use of various automated planning techniques to generate adaptation plans at run time. However, for a planning technique, there is often a trade-off between timeliness and optimality of the solution. For some self-adaptive systems, ideally, one would like to have a planning approach that is both quick and finds an optimal adaptation plan. To find the right balance between these conflicting requirements, this paper introduces a hybrid planning approach that combines more than one planner to obtain the benefits of each. In this paper, to instantiate a hybrid planner we combine deterministic planning with Markov Decision Process (MDP) planning to obtain the best of both worlds: deterministic planning provides plans quickly when timeliness is critical, while allowing MDP planning to generate optimal plans when the system has sufficient time to do so. We validate the hybrid planning approach using a realistic workload pattern in a simulated cloud-based self-adaptive system.

Abstract: Architecture-based approaches to self-adaptation rely on architectural descriptions to reason about the best way of adapting the structure and behavior of software-intensive systems at run time, either by choosing among a set of predefined adaptation strategies, or by automatically generating adaptation plans. Predefined strategy selection has allow computational overhead and facilitates dealing with uncertainty (e.g., by accounting explicitly for contingencies derived from unexpected outcomes of actions), but requires additional designer effort regarding the specification of strategies and is unable to guarantee optimal solutions. In contrast, run time plan generation is able to explore a richer solution space and provide optimal solutions in some cases, but is more limited when dealing with uncertainty, and incurs higher computational overheads. In this paper, we propose an approach to optimal adaptation plan generation for architecture-based self-adaptation via model checking of stochastic multiplayer games (SMGs). Our approach enables: (i) trade-off analysis among different qualities by means of utility functions and preferences, and (ii) explicit modeling of uncertainty in the outcome of adaptation actions and the behavior of the environment. Basing on the concepts embodied in the Rainbow framework for self-adaptation, we illustrate our approach in Znn.com, a case study that reproduces the infrastructure for a news website.

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: Planning is one of the fundamental design considerations when building a selfadaptive software system. Planning helps the adaptive system to determine an appropriate course of action at run time that seeks to change the system's behavior in response to faults, changing environments and security threats. Therefore, having an appropriate planner to find a plan is critical to a successful self-adaptation.

For many adaptive systems, an appropriate planner is the one that not only finds a plan quickly, particularly, in urgent circumstances but also the plan provides a near-optimal long-term performance. However, due to the fundamental trade-off between quality and timeliness of planning, today designers often have to compromise between an approach that finds a plan quickly and an approach that is slow but finds a higher-quality plan.

To deal with this trade-off, this thesis proposes a hybrid planning approach for self-adaptive systems that combines of-the-shelf deliberative and reactive planners to find a balance between quality and timeliness. The key idea is to use reactive planning to provide a quick (although potentially a sub-optimal) response, but simultaneously invoke deliberative planning to determine quality plans. Once the deliberative plan is ready, it takes over the execution from the reactive plan to provide a higher quality adaptation thereafter.

Such a combination of planners can, in principle, reap the benefits of both worlds: providing plans quickly when the timing is critical, while allowing (nearly) optimal plans to be generated when the system has sufficient time to do so. Moreover, instead of going through the non-trivial process of developing a new algorithm/heuristic, hybrid planning combines off-the-shelf planners; therefore, hybrid planning does not require software engineers to master the complexity of developing new planning algorithms/heuristics.

This thesis demonstrates that, compared to its constituent reactive and deliberative planners, hybrid planning can find a better balance between the timeliness and the quality of planning, thereby improve adaptation effectiveness as measured by a multidimensional utility function capturing different dimensions of a system's goal. In the process, the thesis makes contributions to both the theory and the practice of hybrid planning in self-adaptive systems. Specifically, the thesis provides: (a) a formal framework defining the problem of hybrid planning; (b) a practical approach (grounded in the formal model) to apply hybrid planning to self-adaptive systems; (c) informal guidelines and a quantitative approach to help engineers to select an appropriate set of planners to instantiate hybrid planning for a given domain, and (d) evaluation of hybrid planning using two realistic systems to bridge the gap between theory and practice.

Abstract: Planning approaches in self-adaptation face the fundamental trade-off between quality and timeliness of adaptation plans. Due to this trade-off, today designers often have to compromise between a planning approach that is quick to find a plan and an approach that is slow but finds a quality plan. To deal with this trade-off, researchers have proposed a hybrid planning approach that combines more than one planning approaches to find a balance between quality and timeliness. However, the diversity of planning approaches makes the problem of hybrid planning complex and multi-faceted. This paper advances the theory of hybrid planning by formalizing the central concepts and four subproblems of hybrid planning. This formalization can serve as a foundation for not only understanding and comparing existing hybrid planners, but also for developing new hybrid planners in the future. Moreover, to illustrate practicality of the formal model, the paper uses it to analyze two hybrid planning instantiations that have shown to effective in their respective contexts. Furthermore, grounded on the formal model, the paper formalizes explicit/implicit assumptions about these instantiations that must hold for them to be sound.