AI Planning and Scheduling Infusion in Space: ESA Achievements and Perspectives

2010 ◽  
Author(s):  
Alessandro Donati ◽  
Nicola Policella
Keyword(s):  
Ai Planning ◽  
Planning And Scheduling

scholarly journals Preface to special issue on planning and scheduling

2010 ◽  
Vol 25 (3) ◽  
pp. 247-248
Author(s):  
Roman Barták ◽  
Amedeo Cesta ◽  
Lee McCluskey ◽  
Miguel A. Salido
Keyword(s):  
Constraint Satisfaction ◽  
Real World ◽  
Knowledge Engineering ◽  
Real Life ◽  
Ai Planning ◽  
Planning And Scheduling ◽  
Useful Knowledge ◽  
State Dependent ◽  
Life Problems ◽  
Modelling Techniques

AbstractPlanning, scheduling and constraint satisfaction are important areas in artificial intelligence (AI) with broad practical applicability. Many real-world problems can be formulated as AI planning and scheduling (P&S) problems, where resources must be allocated to optimize overall performance objectives. Frequently, solving these problems requires an adequate mixture of planning, scheduling and resource allocation to competing goal activities over time in the presence of complex state-dependent constraints. Constraint satisfaction plays an important role in solving such real-life problems, and integrated techniques that manage P&S with constraint satisfaction are particularly useful. Knowledge engineering supports the solution of such problems by providing adequate modelling techniques and knowledge extraction techniques for improving the performance of planners and schedulers. Briefly speaking, knowledge engineering tools serve as a bridge between the real world and P&S systems.

Editorial for AI planning and scheduling

2009 ◽  
Vol 12 (3) ◽  
pp. 225-226
Author(s):  
Rong Qu ◽  
Maria Fox ◽  
Derek Long
Keyword(s):  
Ai Planning ◽  
Planning And Scheduling

scholarly journals On-line Planning and Scheduling: An Application to Controlling Modular Printers

2011 ◽  
Vol 40 ◽  
pp. 415-468 ◽  
Author(s):  
W. Ruml ◽  
M. B. Do ◽  
R. Zhou ◽  
M. P.J. Fromherz
Keyword(s):  
Intelligent Systems ◽  
High Speed ◽  
Ai Planning ◽  
Line Planning ◽  
Planning And Scheduling ◽  
Space Planning ◽  
On Line ◽  
Control Knowledge ◽  
General Architecture ◽  
Domain Independent

We present a case study of artificial intelligence techniques applied to the control of production printing equipment. Like many other real-world applications, this complex domain requires high-speed autonomous decision-making and robust continual operation. To our knowledge, this work represents the first successful industrial application of embedded domain-independent temporal planning. Our system handles execution failures and multi-objective preferences. At its heart is an on-line algorithm that combines techniques from state-space planning and partial-order scheduling. We suggest that this general architecture may prove useful in other applications as more intelligent systems operate in continual, on-line settings. Our system has been used to drive several commercial prototypes and has enabled a new product architecture for our industrial partner. When compared with state-of-the-art off-line planners, our system is hundreds of times faster and often finds better plans. Our experience demonstrates that domain-independent AI planning based on heuristic search can flexibly handle time, resources, replanning, and multiple objectives in a high-speed practical application without requiring hand-coded control knowledge.

scholarly journals Robots in Retirement Homes: Applying Off-the-Shelf Planning and Scheduling to a Team of Assistive Robots

2017 ◽  
Vol 58 ◽  
pp. 523-590 ◽  
Author(s):  
Tony T. Tran ◽  
Tiago Vaquero ◽  
Goldie Nejat ◽  
J. Christopher Beck
Keyword(s):  
Constraint Programming ◽  
Real World ◽  
Ai Planning ◽  
Retirement Home ◽  
Line Planning ◽  
Planning And Scheduling ◽  
Planning Time ◽  
Assistive Robots ◽  
Real World Application ◽  
Modelling Assumptions

This paper investigates three different technologies for solving a planning and scheduling problem of deploying multiple robots in a retirement home environment to assist elderly residents. The models proposed make use of standard techniques and solvers developed in AI planning and scheduling, with two primary motivations. First, to find a planning and scheduling solution that we can deploy in our real-world application. Second, to evaluate planning and scheduling technology in terms of the ``model-and-solve'' functionality that forms a major research goal in both domain-independent planning and constraint programming. Seven variations of our application are studied using the following three technologies: PDDL-based planning, time-line planning and scheduling, and constraint-based scheduling. The variations address specific aspects of the problem that we believe can impact the performance of the technologies while also representing reasonable abstractions of the real world application. We evaluate the capabilities of each technology and conclude that a constraint-based scheduling approach, specifically a decomposition using constraint programming, provides the most promising results for our application. PDDL-based planning is able to find mostly low quality solutions while the timeline approach was unable to model the full problem without alterations to the solver code, thus moving away from the model-and-solve paradigm. It would be misleading to conclude that constraint programming is ``better'' than PDDL-based planning in a general sense, both because we have examined a single application and because the approaches make different assumptions about the knowledge one is allowed to embed in a model. Nonetheless, we believe our investigation is valuable for AI planning and scheduling researchers as it highlights these different modelling assumptions and provides insight into avenues for the application of AI planning and scheduling for similar robotics problems. In particular, as constraint programming has not been widely applied to robot planning and scheduling in the literature, our results suggest significant untapped potential in doing so.

scholarly journals The 3rd International Planning Competition: Results and Analysis

2003 ◽  
Vol 20 ◽  
pp. 1-59 ◽  
Author(s):  
D. Long ◽  
M. Fox
Keyword(s):  
Statistical Analysis ◽  
Ai Planning ◽  
Relative Difficulty ◽  
Comparative Performance ◽  
Planning And Scheduling ◽  
International Conference ◽  
The Third ◽  
The Future ◽  
Problem Instances ◽  
International Planning

This paper reports the outcome of the third in the series of biennial international planning competitions, held in association with the International Conference on AI Planning and Scheduling (AIPS) in 2002. In addition to describing the domains, the planners and the objectives of the competition, the paper includes analysis of the results. The results are analysed from several perspectives, in order to address the questions of comparative performance between planners, comparative difficulty of domains, the degree of agreement between planners about the relative difficulty of individual problem instances and the question of how well planners scale relative to one another over increasingly difficult problems. The paper addresses these questions through statistical analysis of the raw results of the competition, in order to determine which results can be considered to be adequately supported by the data. The paper concludes with a discussion of some challenges for the future of the competition series.

scholarly journals The Third Competition on Knowledge Engineering for Planning and Scheduling

AI Magazine ◽  
2010 ◽  
Vol 31 (1) ◽  
pp. 95
Author(s):  
Roman Bartak ◽  
Simone Fratini ◽  
Lee McCluskey
Keyword(s):  
Knowledge Engineering ◽  
Formal Description ◽  
Ai Planning ◽  
Application Area ◽  
Planning And Scheduling ◽  
Specific Language ◽  
The Third ◽  
Narrow Focus ◽  
Domain Models ◽  
Language Output

We report on the staging of the third competition on knowledge engineering for AI planning and scheduling systems, held during ICAPS-09 at Thessaloniki, Greece in September 2009. We give an overview of how the competition has developed since its first run in 2005, and its relationship with the AI planning field. This run of the competition focused on translators that when input with some formal description in an application-area-specific language, output solver-ready domain models. Despite a fairly narrow focus within knowledge engineering, seven teams took part in what turned out to be a very interesting and successful competition.

scholarly journals Bridging the gap between planning and scheduling

2000 ◽  
Vol 15 (1) ◽  
pp. 47-83 ◽  
Author(s):  
DAVID E. SMITH ◽  
JEREMY FRANK ◽  
ARI K. JÓNSSON
Keyword(s):  
Artificial Intelligence ◽  
Ai Planning ◽  
Planning And Scheduling ◽  
Complex Interactions ◽  
The Right ◽  
Action Choice

Planning research in Artificial Intelligence (AI) has often focused on problems where there are cascading levels of action choice and complex interactions between actions. In contrast, scheduling research has focused on much larger problems where there is little action choice, but the resulting ordering problem is hard. In this paper, we give an overview of AI planning and scheduling techniques, focusing on their similarities, differences, and limitations. We also argue that many difficult practical problems lie somewhere between planning and scheduling, and that neither area has the right set of tools for solving these vexing problems.

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