47220301
Expert simulation for on-line scheduling
The state-of-the-art in manufacturing has moved toward flexibility, automation and integration. The efforts spent on bringing computer-integrated manufacturing (CIM) to plant floors have been motivated by the overall thrust to increase the speed of new products to market. One of the links in CIM is plant floor scheduling, which is concerned with efficiently orchestrating the plant floor to meet the customer demand and responding quickly to changes on the plant floor and changes in customer demand. The Expert System Scheduler (ESS) has been developed to address this link in CIM. The scheduler utilizes real-time plant information to generate plant floor schedules which honor the factory resource constraints while taking advantage of the flexibility of its components.
The scheduler uses heuristics developed by an experienced human factory scheduler for most of the decisions involved in scheduling. The expertise of the human scheduler has been built into the computerized version using the expert system approach of the discipline of artificial intelligence (AI). Deterministic simulation concepts have been used to develop the schedule and determine the decision points. As such, simulation modeling and AI techniques share many concepts, and the two disciplines can be used synergistically. Examples of some common concepts are the ability of entities to carry attributes and change dynamically (simulation—entities/attributes or transaction/parameters versus AI—frames/slots); the ability to control the flow of entities through a model of the system (simulation—conditional probabilities versus AI—production rules); and the ability to change the model based upon state variables (simulation—language constructs based on variables versus AI—pattern-invoked programs). Shannon [6] highlights similarities and differences between conventional simulation and an AI approach. Kusiak and Chen [3] report increasing use of simulation in development of expert systems.
ESS uses the synergy between AI techniques and simulation modeling to generate schedules for plant floors. Advanced concepts from each of the two areas are used in this endeavor. The expert system has been developed using frames and object-oriented coding which provides knowledge representation flexibility. The concept of “backward” simulation, similar to the AI concept of backward chaining, is used to construct the events in the schedule. Some portions of the schedule are constructed using forward or conventional simulation.
The implementation of expert systems and simulation concepts is intertwined in ESS. However, the application of the concepts from these two areas will be treated separately for ease of presentation. We will first discuss the expert system approach and provide a flavor of the heuristics. The concept of backward simulation and the motive behind it will then be explored along with some details of the implementation and the plant floor where the scheduler is currently being used. We will then highlight some advantages and disadvantages of using the expert simulation approach for scheduling, and, finally, the synergetic relationship between expert systems and simulation.
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.
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.
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.
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.
2224997
On the power of learning robustly
1 Introduction Intuitively, a class of objects is robustly learn-able if not only this class itself is learnable but all of its computable transformations do remain learnable as well. In that sense, being learnable robustly seems to be a desirable property in all fields of learning. We will study this phenomenon within the paradigm of inductive inference. Here a class of recursive functions is called robustly learn-able under a success criterion I iff all of its images under general recursive operators are learnable under the criterion I. Fulk [Fu190] showed the existence of a non-trivial class which is robustly learnable under the criterion Ex. However, several of the hierarchies (such as the anomaly hierarchies for Ex and Bc) do not stand robustly. Hence, up to now it was not clear if robust learning is really rich. The main intention of this paper is to give strong evidence that robust learning is rich. Our main result proved by a priority construction is that the mind change hierarchy for Ex stands robustly. Moreover, the hierarchies of team learning for both Ex and Bc stand robustly as well. In several contexts, we observe the surprising fact that a more complex topo-logical structure of the classes to be learned leads to positive robustness results, whereas an easy topological structure yields negative results. We also show the counter-intuitive fact that even some self-referential classes can be learned robustly. Some of our results point out the difficulty of robust learning when only a bounded number of mind changes is allowed. Further results concerning uniformly robust learning are summarized. Consider the following basic learning scenario. A learning machine has to learn some unknown object, that is, based on some information the machine creates one or more hypotheses which finally yield a correct global description of that object. Then consider the extent to which such a machine can be a general purpose learner in that it can learn each object from a, finite or even infinite , class of objects. In various learning models this can be shown to be possible. A next question to ask then could be the following: How " stable " is the property that a class is learnable? That is, under which, small or not so small, " transformations " the transformed classes remain learnable? Of course, this may depend on the class under consideration; some classes may be more stable in that …
39173766
Synthesizing Learners Tolerating Computable Noisy Data
An index for an r.e. class of languages (by definition) generates a sequence of grammars defining the class. An index for an indexed family of recursive languages (by definition) generates a sequence of decision procedures defining the family. F. Stephan's model of noisy data is employed, in which, roughly, correct data crops up infinitely often and incorrect data only finitely often. In a computable universe, all data sequences, even noisy ones, are computable. New to the present paper is the restriction that noisy data sequences be, nonetheless, computable. This restriction is interesting since we may live in a computable universe. Studied, then, is the synthesis from indices for r.e. classes and for indexed families of recursive languages of various kinds of noise-tolerant language-learners for the corresponding classes or families indexed, where the noisy input data sequences are restricted to being computable. Many positive results, as well as some negative results, are presented regarding the existence of such synthesizers. The main positive result is: grammars for each indexed family can be learned behaviorally correctly from computable, noisy, positive data. The proof of another positive synthesis result yields, as a pleasant corollary, a strict subset-principle or telltale style characterization, for the computable noise-tolerant behaviorally correct learnability of grammars from positive and negative data, of the corresponding families indexed.
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
205926547
Non-U-shaped vacillatory and team learning
U-shaped learning behaviour in cognitive development involves learning, unlearning and relearning. It occurs, for example, in learning irregular verbs. The prior cognitive science literature is occupied with how humans do it, for example, general rules versus tables of exceptions. This paper is mostly concerned with whether U-shaped learning behaviour may be necessary in the abstract mathematical setting of inductive inference, that is, in the computational learning theory following the framework of Gold. All notions considered are learning from text, that is, from positive data. Previous work showed that U-shaped learning behaviour is necessary for behaviourally correct learning but not for syntactically convergent, learning in the limit (= explanatory learning). The present paper establishes the necessity for the hierarchy of classes of vacillatory learning where a behaviourally correct learner has to satisfy the additional constraint that it vacillates in the limit between at most b grammars, where [email protected]?{2,3,...,*}. Non-U-shaped vacillatory learning is shown to be restrictive: every non-U-shaped vacillatorily learnable class is already learnable in the limit. Furthermore, if vacillatory learning with the parameter b=2 is possible then non-U-shaped behaviourally correct learning is also possible. But for b=3, surprisingly, there is a class witnessing that this implication fails.
30237295
Crowd Control: Coordinating Processes in Parallel
Tutorials • Bridget Rosewell and Paul Ormerod " Agent Based Models and Social Simulation in the Policy Process " [Room: Aula Magna] This tutorial will focus on practical examples of agent based models which are actually being used as part of the decision making process of policy makers. The tutorial will also address key issues in integrating ABMs and social simulation into the policy making process. The tutorial will introduce the Multi-Agent Simulation Suite (MASS), a novel, integrated simulation environment for agent-based social simulation. MASS was designed with scientific modelers and not professional programmers in mind. Therefore, ease of use and prudent scientific practices were at the focus of the suite's design. The tutorial consists in a brief introductory part providing a generalized definition of the basic elements composing a simulation model. Then, it will be shown how LSD allows users to transparently adopt the theoretical definition, automatically generating all the technical code necessary for developing, running and analysing a simulation model. A series of examples will show the advantages of the definition proposed and the most relevant features of LSD. The tutorial will introduce the Multi-Agent Simulation Suite (MASS), a novel, integrated simulation environment for agent-based social simulation. MASS was designed with scientific modelers and not professional programmers in mind. Therefore, ease of use and prudent scientific practices were at the focus of the suite's design.