39 research outputs found
Learning to improve iterative repair scheduling
This paper presents a general learning method for dynamically selecting between repair heuristics in an iterative repair scheduling system. The system employs a version of explanation-based learning called Plausible Explanation-Based Learning (PEBL) that uses multiple examples to confirm conjectured explanations. The basic approach is to conjecture contradictions between a heuristic and statistics that measure the quality of the heuristic. When these contradictions are confirmed, a different heuristic is selected. To motivate the utility of this approach we present an empirical evaluation of the performance of a scheduling system with respect to two different repair strategies. We show that the scheduler that learns to choose between the heuristics outperforms the same scheduler with any one of two heuristics alone
Iterative repair for scheduling and rescheduling
An iterative repair search method is described called constraint based simulated annealing. Simulated annealing is a hill climbing search technique capable of escaping local minima. The utility of the constraint based framework is shown by comparing search performance with and without the constraint framework on a suite of randomly generated problems. Results are also shown of applying the technique to the NASA Space Shuttle ground processing problem. These experiments show that the search methods scales to complex, real world problems and reflects interesting anytime behavior
Working Notes from the 1992 AAAI Spring Symposium on Practical Approaches to Scheduling and Planning
The symposium presented issues involved in the development of scheduling systems that can deal with resource and time limitations. To qualify, a system must be implemented and tested to some degree on non-trivial problems (ideally, on real-world problems). However, a system need not be fully deployed to qualify. Systems that schedule actions in terms of metric time constraints typically represent and reason about an external numeric clock or calendar and can be contrasted with those systems that represent time purely symbolically. The following topics are discussed: integrating planning and scheduling; integrating symbolic goals and numerical utilities; managing uncertainty; incremental rescheduling; managing limited computation time; anytime scheduling and planning algorithms, systems; dependency analysis and schedule reuse; management of schedule and plan execution; and incorporation of discrete event techniques
Artificial Intelligence Research Branch future plans
This report contains information on the activities of the Artificial Intelligence Research Branch (FIA) at NASA Ames Research Center (ARC) in 1992, as well as planned work in 1993. These activities span a range from basic scientific research through engineering development to fielded NASA applications, particularly those applications that are enabled by basic research carried out in FIA. Work is conducted in-house and through collaborative partners in academia and industry. All of our work has research themes with a dual commitment to technical excellence and applicability to NASA short, medium, and long-term problems. FIA acts as the Agency's lead organization for research aspects of artificial intelligence, working closely with a second research laboratory at the Jet Propulsion Laboratory (JPL) and AI applications groups throughout all NASA centers. This report is organized along three major research themes: (1) Planning and Scheduling: deciding on a sequence of actions to achieve a set of complex goals and determining when to execute those actions and how to allocate resources to carry them out; (2) Machine Learning: techniques for forming theories about natural and man-made phenomena; and for improving the problem-solving performance of computational systems over time; and (3) Research on the acquisition, representation, and utilization of knowledge in support of diagnosis design of engineered systems and analysis of actual systems
Optimal Planning with State Constraints
In the classical planning model, state variables are assigned
values in the initial state and remain unchanged unless
explicitly affected by action effects. However, some properties
of states are more naturally modelled not as direct effects of
actions but instead as derived, in each state, from the primary
variables via a set of rules. We refer to those rules as state
constraints. The two types of state constraints that will be
discussed here are numeric state constraints and logical rules
that we will refer to as axioms.
When using state constraints we make a distinction between
primary variables, whose values are directly affected by action
effects, and secondary variables, whose values are determined by
state constraints. While primary variables have finite and
discrete domains, as in classical planning, there is no such
requirement for secondary variables. For example, using numeric
state constraints allows us to have secondary variables whose
values are real numbers. We show that state constraints are a
construct that lets us combine classical planning methods with
specialised solvers developed for other types of problems. For
example, introducing numeric state constraints enables us to
apply planning techniques in domains involving interconnected
physical systems, such as power networks.
To solve these types of problems optimally, we adapt commonly
used methods from optimal classical planning, namely state-space
search guided by admissible heuristics. In heuristics based on
monotonic relaxation, the idea is that in a relaxed state each
variable assumes a set of values instead of just a single value.
With state constraints, the challenge becomes to evaluate the
conditions, such as goals and action preconditions, that involve
secondary variables. We employ consistency checking tools to
evaluate whether these conditions are satisfied in the relaxed
state. In our work with numerical constraints we use linear
programming, while with axioms we use answer set programming and
three value semantics. This allows us to build a relaxed planning
graph and compute constraint-aware version of heuristics based on
monotonic relaxation.
We also adapt pattern database heuristics. We notice that an
abstract state can be thought of as a state in the monotonic
relaxation in which the variables in the pattern hold only one
value, while the variables not in the pattern simultaneously hold
all the values in their domains. This means that we can apply the
same technique for evaluating conditions on secondary variables
as we did for the monotonic relaxation and build pattern
databases similarly as it is done in classical planning.
To make better use of our heuristics, we modify the A* algorithm
by combining two techniques that were previously used
independently – partial expansion and preferred operators. Our
modified algorithm, which we call PrefPEA, is most beneficial in
cases where heuristic is expensive to compute, but accurate, and
states have many successors
On the Combination of Game-Theoretic Learning and Multi Model Adaptive Filters
This paper casts coordination of a team of robots within the framework of game theoretic learning algorithms. In particular a novel variant of fictitious play is proposed, by considering multi-model adaptive filters as a method to estimate other players’ strategies. The proposed algorithm can be used as a coordination mechanism between players when they should take decisions under uncertainty. Each player chooses an action after taking into account the actions of the other players and also the uncertainty. Uncertainty can occur either in terms of noisy observations or various types of other players. In addition, in contrast to other game-theoretic and heuristic algorithms for distributed optimisation, it is not necessary to find the optimal parameters a priori. Various parameter values can be used initially as inputs to different models. Therefore, the resulting decisions will be aggregate results of all the parameter values. Simulations are used to test the performance of the proposed methodology against other game-theoretic learning algorithms.</p
Customer Information Driven After Sales Service Management: Lessons from Spare Parts Logistics
Over the years, after sales service business in capital goods and high tech sectors has experienced significant growth. The drivers for growth are higher service profits, increased competitions, and primary market contractions. The enablers for growth include information driven service processes and a move from one size fit all oriented warranty contracts to service level agreement offerings that differ in service prices and response guarantees. Although, these trends provide an opportunity to the service providers to match their service resources to the time varying service requirements of a heterogeneous customer base, the tools and techniques to support decision makers are lacking as of to date. In this thesis, we aim to make a small contribution in closing this gap. We gain business environment related insights of after sales service by studying it at a major computer equipment manufacturer. After sales service is a complex task that is accomplished by making a series of strategic, tactical, and operational decisions in maintenance services management, spare parts logistics management and spare part returns management. We exclusively focus on operational and tactical decisions in spare parts logistics management. We identify that customer information, or more specifically installed base information is a valuable source to support spare parts logistics decisions at the operational and tactical levels. We present an execution technique for spare parts logistics that uses installed base information to provide differentiated service to a heterogeneous customer base and results in additional profits for the service provider. Finally, we study execution decisions in spare parts logistics and spare part returns management for their interrelation. We highlight that explicit consideration of this interrelation yields additional benefits
Rolling Stock Rescheduling in Passenger Railways: Applications in short-term planning and in disruption management
Modern society is highly dependent on a reliable railway system for workforce mobility and easy access to the cities. However, the daily operations of a large passenger railway system are subject to unexpected disruptions such as rolling stock breakdowns or malfunctioning infrastructure. In a disrupted situation, the railway operator must adapt the timetable, rolling stock and crew to the modified conditions. This adaptation of resource allocations requires the solution of complex combinatorial problems in very short time and thus represents a major challenge for the involved dispatchers.
In this thesis we develop models and solution methods for the rescheduling of the rolling stock during disruptions. The models incorporate service aspects (such as seat capacity), efficiency aspects (such as number of kilometers driven by the rolling stock), and process related aspects (such as the need for night-time relocation of rolling stock).
The thesis contains applications of the developed models in three different contexts. First, we present a framework for applying the rescheduling models in the highly uncertain environment of railway disruption management, and we demonstrate the trade-off between computation time and solution quality. Second, we embed the rolling stock rescheduling models in a simulation framework to account for the dynamic passenger behavior during disruptions. This framework allows us to significantly decrease the delays experienced by passengers. Third, we apply the rescheduling models to real-life planning problems from the short-term planning department of the Netherlands Railways. The models lead to a considerable speed-up of the process and significant savings