32884753
Expert Simulation For On-line Scheduling
In recent years, the automotive industry has realized the importance of speed of new products to market and has mounted efforts for improving it. The Expert System Scheduler (ESS) facilitates these efforts by enabling manufacturing plants to generate viable schedules under increasing constraints and demands for flexibility. The scheduler takes advantage of the Computer Integrated Manufacturing (CIM) environment by utilizing the real-time information from the factory for responsive scheduling. The Expert System Scheduler uses heuristics developed by an experiences factory scheduler. It uses simulation concepts and these heuristics to generate schedules. Forward and "backward" simulation are used at different stages of the schedule generation process. The system is used to control parts flow on the factory floor at one automated facility. This highly automated facility is a testbed for implementation of CIM concepts. The scheduler runs on a Texas Instruments (TI) Explorer II computer using software developed inhouse utilizing IntelliCorp's Knowledge Engineering Environment (KEE) shell and the LISP language. The scheduling computer is networked to the factory control computer, which actually controls the plant floor. The TI Explorer II acquires current plant floor information from the factory control system, generates a new schedule and sends it back within a short time. The configuration allows fast response to changes in requirements and plant floor conditions.
201885623
Manufacturing supply chain applications: modeling computer assembly operations for supply chain integration
Factory operations have been modeled for years to understand the relationship between the different design and policy factors and the performance measures of interest. The increasing awareness of the need to manage factories as a link in the supply chain places a corresponding requirement for an enhanced approach for factory modeling. This paper describes the modeling of a computer assembly factory for supply chain integration by including aspects of inbound and outbound logistics and relevant business processes. Lessons are drawn based on the experience.
6477912
Stress testing a supply chain using simulation
Simulation provides the capability to evaluate performance of a system operating under current or proposed configurations, policies and procedures. It is very applicable to evaluation of strategic and operational level plans for supply chains. It is especially useful for exploring the viability of a supply chain before beginning production. This paper describes the use of simulation for determining the readiness of a supply chain designed by a small company for providing sub-systems to a defense contractor. The supply chain had to be stress tested at surge and mobilization volume levels to meet the requirements. Simulation provided the ideal methodology to identify and demonstrate the behavior of the supply chain under stress and evaluate strategies to meet the defined volumes. It was used to prove the readiness of the supply chain reducing perceived risks in using the virtual operation.
7194511
A Concept Prototype for Integrated Gaming and Simulation for Incident Management
This paper describes a prototype that has been developed to demonstrate the concept of integrated gaming and simulation for incident management. An architecture for the purpose was developed and presented at the last conference. A hypothetical emergency incident scenario has been developed for demonstrating the applicability of integrated simulation and gaming. A number of simulation and gaming modules have been utilized to model the major aspects of the hypothetical scenario. The modules demonstrate the value of utilizing simulation for incident management applications. They can be used to highlight the value of simulation and gaming for training applications in particular. Two of the simulation modules have been integrated using a modified implementation of the high level architecture to give an idea of the advantages. Technical issues in integration are identified
18211780
Supply chain opportunities: panel session: opportunities for simulation in supply chain management
It has become a matter of survival that many companies improve their supply chain efficiency. This presents an opportunity for simulation. However, there are many challenges that must be overcome for simulation to be a contributor to play an effective role. Four contributors discuss the opportunities that they see for simulation to play a meaningful role in the area of supply chain management.
18971661
Development of a high-level supply chain simulation model
This paper describes an effort that involved development of a simulation model for evaluating the business processes and inventory control parameters of a logistics and distribution supply chain. A generic simulation tool, rather than a supply chain simulator, was developed for meeting customized needs of the effort. The paper describes, the approaches used to model at the selected level of abstraction, the development of interfaces for data and experimentation and the development and delivery of animation for communicating the approach and results to the client.
40202681
Classes with easily learnable subclasses
In this paper we study the question of whether identifiable classes have subclasses which are identifiable under a more restrictive criterion. The chosen framework is inductive inference, in particular the criterion of explanatory learning (Ex) of recursive functions as introduced by Gold [Inform. Comput. 10 (1967) 447]. Among the more restrictive criteria is finite learning where the learner outputs, on every function to be learned, exactly one hypothesis (which has to be correct). The topic of the present paper are the natural variants (a) and (b) below of the classical question whether a given learning criterion like finite learning is more restrictive than Ex-learning. (a) Does every infinite Ex-identifiable class have an infinite finitely identifiable subclass? (b) If an infinite Ex-identifiable class S has an infinite finitely identifiable subclass, does it necessarily follow that some appropriate learner Ex-identifies S as well as finitely identifies an infinite subclass of S? These questions are also treated in the context of ordinal mind change bounds.
513279
Iterative Learning of Simple External Contextual Languages
It is investigated for which choice of a parameter q, denoting the number of contexts, the class of simple external contextual languages is iteratively learnable. On one hand, the class admits, for all values of q, polynomial time learnability provided an adequate choice of the hypothesis space is given. On the other hand, additional constraints like consistency and conservativeness or the use of a one-one hypothesis space changes the picture -- iterative learning limits the long term memory of the learner to the current hypothesis and these constraints further hinder storage of information via padding of this hypothesis. It is shown that if q> 3, then simple external contextual languages are not iteratively learnable using a class preserving one-one hypothesis space, while for q= 1 it is iteratively learnable, even in polynomial time. For the intermediate levels, there is some indication that iterative learnability using a class preserving one-one hypothesis space might depend on the size of the alphabet. It is also investigated for which choice of the parameters, the simple external contextual languages can be learnt by a consistent and conservative iterative learner.
42436517
Language Learning by a "Team" (Extended Abstract)
Traditionally, research in the reinforcement learning (RL) community has been devoted to developing domain-independent algorithms such as SARSA [13], Q-learning [16], prioritized sweeping [8], or LSPI [6], that are designed to work for any given state space and action space. However, the modus operandi in RL research has been for a human expert to re-code each learning environment, including defining the actions and state features, as well as specifying the algorithm to be used. Typically each new RL experiment is run by explicitly calling a new program (even when learning can be biased by previous learning experiences, as in transfer learning [10, 15, 14]). Thus, while standards have developed for describing and testing individual RL algorithms (e.g., RL-Glue [17]), no such standards have developed for the problem of describing complete tasks to a preexisting agent. In this paper we present a new language for specifying complete tasks, and a framework for agents to learn a new policy for solving these tasks. This language was designed for the large, multi-year, multi-institution“Bootstrap Learning” (BL) project [1], which aims to enable humans to teach agents multiple different tasks using different instructional techniques or sources of training data. Our “BL Task Learning” (or BLTL) language, specific for sequential decision making tasks, allows the human teacher to specify to the agent starting states, reward functions, termination conditions, advice [7, 5] or demonstrations [9], indicate relevant previous experience (enabling transfer learning [10, 15, 14]), use previously taught tasks as primitive actions in new tasks, or specify portions of a more complex task which are to be refined by learning (enabling task decomposition[2, 3, 12]). Because parts of larger tasks can be learned each with a different technique, we enable multiple learning methods to be Cite as: A General Task Specification Language for Bootstrap Learning (Extended Abstract), Ian Fasel, Michael Quinlan, Peter Stone, Proc. of 8th Int. Conf. on Autonomous Agents and Multiagent Systems (AAMAS 2009), Decker, Sichman, Sierra and Castelfranchi (eds.), May, 10–15, 2009, Budapest, Hungary, pp. XXX-XXX. Copyright c © 2009, International Foundation for Autonomous Agents and Multiagent Systems (www.ifaamas.org). All rights reserved. synergistically integrated in a single agent that is far more capable than an agent using any one learning algorithm. 2. LANGUAGE PRIMITIVES
15761885
Elementary formal systems, intrinsic complexity, and procrastination
Recently, rich subclasses of elementary formal systems (EFS) have been shown to be identifiable in the limit from only positive data. Examples of these classes are Angluin’s pattern languages, unions of pattern languages by Wright and Shinohara, and classes of languages definable by length-bounded elementary formal systems studied by Shinohara. The present paper employs two distinct bodies of abstract studies in the inductive inference literature to analyze the learnability of the these concrete classes. The first approach, introduced by Freivalds and Smith, uses constructive ordinals to bound the number of mind changes. w denotes the first limit ordinal. An ordinal mind change bound of u means that identification can be carried out by a learner that after examining some element(s) of the language announces an upper bound on the number of mind changes it will make before converging; a bound of 2W means that the learner reserves the right to revise this upper bound once; a bound of 3W means the learner reserves the right to revise this upper bound twice, and so on. A bound of W2 means that identification can be carried out by a learner that announces an upper bound on the number of times it may revise its conjectured upper bound on the number of mind changes. It is shown in the present paper that the ordinal mind change complexity for identification of languages formed by unions of up to n pattern languages is Wn. It is also shown that this bound is essential. Similar results are also Permission to make digital/hard copies of all or part of this material for personal or classroom use is granted without fee provided that rhe copies are not made or distributed for profit or commercial advantage, the copyright ootice, the title of the publication and its date appear, and notice ia given that copyright is by permission of the ACM, Inc. To copy otherwise, to republish, to post on servers or to redistribute to lists, requirea apecitic permiaaion and/or fee. COLT’96, Desenzano del Garda, Italy @ 1996 ACM 0-89791-81 1-8/96/06 ..$3.50 shown to hold for classes definable by lengthbounded elementary formal systems with up to n clauses. The second approach, studied by Freivalds, Kinber and Wiehagen and by Jain and Sharma, employs reductions to study the intrinsic complexity of learnable classes. It is shown that the class of languages formed by taking unions of up to n + 1 pattern languages is a strictly more difficult learning problem than the class of languages formed by the union of up to n pattern languages. It is also shown that a similar hierarchy holds for the bound on the number of clauses in the case of languages definable by length-bounded EFS. In addition to building bridges between three distinct areas of inductive inference, viz., learnability of EFS subclasses, ordinal mind change complexity, and intrinsic complexity, this paper also presents results that relate topological properties of learnable classes to that of intrinsic complexity and ordinal mind change complexity. For example, it is shown that a clam that is complete according to the reductions for intrinsic complexity has infinite elasticity, Since EFS languages and their learnability results have counterparts in traditional logic programming, the present paper demonstrates the possibility of using abstract results of inductive inference to gain insights into inductive logic programming.
36978157
Vacillatory and BC Learning on Noisy Data