31 research outputs found
Learning Effective Changes for Software Projects
The primary motivation of much of software analytics is decision making. How
to make these decisions? Should one make decisions based on lessons that arise
from within a particular project? Or should one generate these decisions from
across multiple projects? This work is an attempt to answer these questions.
Our work was motivated by a realization that much of the current generation
software analytics tools focus primarily on prediction. Indeed prediction is a
useful task, but it is usually followed by "planning" about what actions need
to be taken. This research seeks to address the planning task by seeking
methods that support actionable analytics that offer clear guidance on what to
do. Specifically, we propose XTREE and BELLTREE algorithms for generating a set
of actionable plans within and across projects. Each of these plans, if
followed will improve the quality of the software project.Comment: 4 pages, 2 figures. This a submission for ASE 2017 Doctoral Symposiu
A taxonomy of preferences for physically assistive robots
© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Assistive devices and technologies are getting common and some commercial products are starting to be available. However, the deployment of robots able to physically interact with a person in an assistive manner is still a challenging problem. Apart from the design and control, the robot must be able to adapt to the user it is attending in order to become a useful tool for caregivers. This robot behavior adaptation comes through the definition of user preferences for the task such that the robot can act in the user’s desired way. This article presents a taxonomy of user preferences for assistive scenarios, including physical interactions, that may be used to improve robot decision-making algorithms. The taxonomy categorizes the preferences based on their semantics and possible uses. We propose the categorization in two levels of application (global and specific) as well as two types (primary and modifier). Examples of real preference classifications are presented in three assistive tasks: feeding, shoe fitting and coat dressing.Peer ReviewedPostprint (author's final draft
Priority-Based PlaybookTM Tasking for Unmanned System Teams
We are developing real-time planning and control systems that allow a single human operator to control a team of unmanned aerial vehicles (UAVs). If the operator requests more tasks than can be immediately addressed by the available UAVs, our planning system must choose which goals to try to achieve, and which to postpone for later effort. To make this decision-making easily understandable and controllable, we allow the user to assign strict priorities to goals, ensuring that if a goal is assigned the highest priority, the system will use every resource available to try to build a successful plan to achieve that goal. In this paper we show how unique features of the SHOP2 hierarchical task network planner permit an elegant implementation of this priority queue behavior. Although this paper is primarily about the technique itself, rather than SHOP2’s performance, we assess the scalability of this priority queue approach and discuss potential directions for improvement, as well as more general forms of meta-control within SHOP2 domains. I
Probabilistic contingent planning based on HTN for high-quality plans
Deterministic planning assumes that the planning evolves along a fully
predictable path, and therefore it loses the practical value in most real
projections. A more realistic view is that planning ought to take into
consideration partial observability beforehand and aim for a more flexible and
robust solution. What is more significant, it is inevitable that the quality of
plan varies dramatically in the partially observable environment. In this paper
we propose a probabilistic contingent Hierarchical Task Network (HTN) planner,
named High-Quality Contingent Planner (HQCP), to generate high-quality plans in
the partially observable environment. The formalisms in HTN planning are
extended into partial observability and are evaluated regarding the cost. Next,
we explore a novel heuristic for high-quality plans and develop the integrated
planning algorithm. Finally, an empirical study verifies the effectiveness and
efficiency of the planner both in probabilistic contingent planning and for
obtaining high-quality plans.Comment: 10 pages, 1 figur
A survey on preferences of quality attributes in the decision-making for self-adaptive systems:The bad, the good and the ugly
Different techniques have been used to specify preferences for quality attributes and decision-making strategies of self-adaptive systems (SAS). These preferences are defined during requirement specification and design time. Further, it is well known that correctly identifying the preferences associated with the quality attributes is a major difficulty. This is exacerbated in the case of SAS, as the preferences defined at design time may not apply to contexts found at runtime. This paper aims at making an exploration of the research landscape that have addressed decision-making and quality attribute preferences specification for selfadaptation, in order to identify new techniques that can improve the current state-of-the-art of decision-making to support self-adaptation. In this paper we (1) review different techniques that support decisionmaking for self-adaptation and identify limitations with respect to the identification of preferences and weights (i.e. the research gap), (2) identify existing solutions that deal with current limitations
Robotic Planning under Hierarchical Temporal Logic Specifications
Past research into robotic planning with temporal logic specifications,
notably Linear Temporal Logic (LTL), was largely based on singular formulas for
individual or groups of robots. But with increasing task complexity, LTL
formulas unavoidably grow lengthy, complicating interpretation and
specification generation, and straining the computational capacities of the
planners. In order to maximize the potential of LTL specifications, we
capitalized on the intrinsic structure of tasks and introduced a hierarchical
structure to LTL specifications. In contrast to the "flat" structure, our
hierarchical model has multiple levels of compositional specifications and
offers benefits such as greater syntactic brevity, improved interpretability,
and more efficient planning. To address tasks under this hierarchical temporal
logic structure, we formulated a decomposition-based method. Each specification
is first broken down into a range of temporally interrelated sub-tasks. We
further mine the temporal relations among the sub-tasks of different
specifications within the hierarchy. Subsequently, a Mixed Integer Linear
Program is utilized to generate a spatio-temporal plan for each robot. Our
hierarchical LTL specifications were experimentally applied to domains of
robotic navigation and manipulation. Results from extensive simulation studies
illustrated both the enhanced expressive potential of the hierarchical form and
the efficacy of the proposed method.Comment: 8 pages, 4 figure
Feature Model Configuration Based on Two-Layer Modelling in Software Product Lines
The aim of the Software Product Line (SPL) approach is to improve the software development process by producing software products that match the stakeholders’ requirements. One of the important topics in SPLs is the feature model (FM) configuration process. The purpose of configuration here is to select and remove specific features from the FM in order to produce the required software product. At the same time, detection of differences between application’s requirements and the available capabilities of the implementation platform is a major concern of application requirements engineering. It is possible that the implementation of the selected features of FM needs certain software and hardware infrastructures such as database, operating system and hardware that cannot be made available by stakeholders. We address the FM configuration problem by proposing a method, which employs a two-layer FM comprising the application and infrastructure layers. We also show this method in the context of a case study in the SPL of a sample E-Shop website. The results demonstrate that this method can support both functional and non-functional requirements and can solve the problems arising from lack of attention to implementation requirements in SPL FM selection phase
HAUTO: Automated composition of convergent services based in HTN planning
This paper presents HAUTO, a framework able to compose convergent services automatically. HAUTO is based in HTN (hierarchical task networks) Automated Planning and is composed of three modules: a request processing module that transforms natural language and context information into a planning instance, the automated composition module based on HTN planning and the execution environment for convergent (Web and telecom) services. The integration of a planning component provides two basic functionalities: the possibility of customizing the composition of services using the user context information and a middleware level that integrates the execution of services in high performance telecom environments. Finally, a prototype in environmental early warning management is presented as a test case