EGGG, the Extensible Graphical Game Generator, is an experiment in automated programming. By concentrating on a particular domain - games - EGGG allows users to create applications with a minimum of programming effort...
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EGGG, the Extensible Graphical Game Generator, is an experiment in automated programming. By concentrating on a particular domain - games - EGGG allows users to create applications with a minimum of programming effort. We codified the similarities among games and game programs into reusable software components that decouple the rules of a game from its implementation. As a consequence, users can create games merely by describing the rules to EGGG, which then generates a fully functioning game program. In this paper, we survey the design and implementation of EGGG and provide some examples of games that can be created with the system.
An overview of evolutionary algorithms is presented covering genetic algorithms, evolution strategies, genetic programming and evolutionary programming. The schema theorem is reviewed and critiqued. Gray codes, bit re...
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An overview of evolutionary algorithms is presented covering genetic algorithms, evolution strategies, genetic programming and evolutionary programming. The schema theorem is reviewed and critiqued. Gray codes, bit representations and real-valued representations are discussed for parameter optimization problems. Parallel Island models are also reviewed, and the evaluation of evolutionary algorithms is discussed. (C) 2001 Elsevier Science B.V. All rights reserved.
This paper addresses the problem of automated synthesis of ladder diagrams for programmable logic controllers (PLC). The programs are obtained from control specifications expressed as Timed Interpreted Petri Nets (TIP...
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ISBN:
(纸本)0780367227
This paper addresses the problem of automated synthesis of ladder diagrams for programmable logic controllers (PLC). The programs are obtained from control specifications expressed as Timed Interpreted Petri Nets (TIPN). The approach herein presented allows to rapidly synthesize correct programs for PLC;it is based on a set of simple translation rules that produces Ladder Diagram (LD) from a TIPN. Moreover this set of rules was coded into a programming to automatize this process.
This article describes the Multimission VICAR Planner (MVP): an Al planning system which uses knowledge about image processing steps and their requirements to construct executable image processing scripts to support h...
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This article describes the Multimission VICAR Planner (MVP): an Al planning system which uses knowledge about image processing steps and their requirements to construct executable image processing scripts to support high-level science requests made to the Jet Propulsion Laboratory (JPL) Multimission Image Processing Subsystem (MIPS). This article describes a general Al planning approach to automation and application of the approach to a specific area of image processing for planetary science applications involving radiometric correction, color triplet reconstruction, and mosaicing in which the MVP system significantly reduces the amount of effort required by image processing experts to fill a typical request.
A new language system, the automated PROGRAMMER, has been designed and implemented. It aims to automate a great deal of routine effort for scientific, engineering, and mathematical application programming. Its notatio...
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A new language system, the automated PROGRAMMER, has been designed and implemented. It aims to automate a great deal of routine effort for scientific, engineering, and mathematical application programming. Its notation is modeled after conventional textbook mathematical representation so that mathematical expressions may be entered just as they appear in conventional solution specifications. It employs a flexible vocabulary and syntax that emulates technical English. This approach enhances self-documentation, diminishes programming error, and eases maintainability and verifiability. System design is highly user-oriented so as to make the system easy to learn and use. Programs are input via a two-dimensional screen editor using a four-case English, Greek, and mathematical symbol alphabet. Lexical restrictions are minimal, keyword synonyms are available, and various common synonymous syntactic structures, mimicking technical English, are acceptable. Matrix arithmetic is accomplished using standard textbook notation. Powerful input and output facilities are provided.
Visualization is an important component of modern computing. By animating the course of an algorithm’s temporal execution, many key features can be elucidated. We have developed a general framework, termed Call-Graph...
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Visualization is an important component of modern computing. By animating the course of an algorithm’s temporal execution, many key features can be elucidated. We have developed a general framework, termed Call-Graph Caching (CGC), for automating the construction of many complex AI algorithms. By incorporating visualization into CGC interpreters, principled animations can be automatically displayed as AI computations unfold. (1) Systems that support the automatic animation of AI algorithms must address these three design issues: (2) How to represent AI data structures in a general, uniform way that leads to perspicuous animation and efficient redisplay. (3) How to coordinate the succession of graphical events. (4) How to partition AI graphs to provide for separate, uncluttered displays. CGC provides a natural and effective solution to all these concerns. (5) We describe the CGC method, including detailed examples, and motivate why CGC works well for animation. We discuss the CACHE system, our CGC environment for AI algorithm animation. We demonstrate the animation of several AI algorithms – RETE match, linear unification, arc consistency, chart parsing, and truth maintenance – all of which have been implemented in CACHE. Finally, we discuss the application of these methods to interactive interfaces for intelligent systems, using molecular genetics as an example domain.
Traditionally, most industrial robots are programmed by teaching. Automatic planning of robotic tasks has many potential benefits for flexible automation. It allows the user to describe a task to the robot programming...
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Traditionally, most industrial robots are programmed by teaching. Automatic planning of robotic tasks has many potential benefits for flexible automation. It allows the user to describe a task to the robot programming system in a formal and natural manner, and reduces the time required to generate and update robot programs. Two main levels of abstraction in describing robot tasks can be identified. Robot-level programming is based on robot movements and actions, as detailed by the programmer. Object-level or task-level programming allows the user to describe assembly tasks in terms of operations performed on objects being manipulated instead of specifying the individual motions of the robot end-effector. However, commercially available robot-level programming languages still fall short of the robot user's need to programme complex tasks and consequently are not widely used in industry. There is an increasing need for integrating sensors feedback into the robot system to provide better perception and for improving the capacity of the robot to reason and make decisions intelligently in real-time. Task-level programming represents the highest level of abstraction and is the most attractive, as it uses reasoning capabilities provided by Artificial Intelligence. To date, no system of this class has been completely implemented in industry. This paper reviews the progress made in robot programming and task planning systems in the last twenty years, and discusses the current research trends.
RETE match for production systems and Arc Consistency (AC) filtering are two efficient AI algorithms that are designed for particular constraint satisfaction problems (CSP). Interestingly, it is possible to integrate ...
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RETE match for production systems and Arc Consistency (AC) filtering are two efficient AI algorithms that are designed for particular constraint satisfaction problems (CSP). Interestingly, it is possible to integrate the two within a common framework, and provide RETE with the lookahead advantages of AC. Unfortunately, the resulting quadratic dependence of AC on working memory (WM) size precludes the application of AC to RETE, since RETE responds to incremental changes in WM. Recent results in AC graph factorization, however, reduce the quadratic dependence to just linear for certain classes of problems, including RETE. In this paper, we present RETE and AC within the unified framework of Call-Graph Caching (CGC) evaluation. We describe factored arc consistency (FAC) and its use in an integrated FAC/RETE algorithm that provides RETE match with AC lookahead. We discuss our implementations, promising initial empirical results, and explore FAC/RETE’s applicability conditions and extensions. We conclude that incorporating factored AC into RETE match is an interesting and potentially useful application of arc consistency methods to the RETE CSP.
Learning to edit code automatically is becoming more and more feasible. Thanks to recent advances in Neural Machine Translation (NMT), various case studies are being investigated where patches are automatically produc...
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Learning to edit code automatically is becoming more and more feasible. Thanks to recent advances in Neural Machine Translation (NMT), various case studies are being investigated where patches are automatically produced and assessed either automatically (using test suites) or by developers themselves. An appealing setting remains when the developer must provide a natural language input of the requirement for the code change. A recent proof of concept in the literature showed that it is indeed feasible to translate these natural language requirements into code changes. A recent advancement, MODIT [8], has shown promising results in code editing by leveraging natural language, code context, and location information as input. However, it struggles when location information is unavailable. While several studies [29, 81] have demonstrated the ability to edit source code without explicitly specifying the edit location, they still tend to generate edits with less accuracy at the line level. In this work, we address the challenge of generating code edits without precise location information, a scenario we consider crucial for the practical adoption of NMT in code development. To that end, we develop a novel joint training approach for both localization and source code editions. Building a benchmark based on over 70k commits (patches and messages), we demonstrate that our jLED (joint Localize and EDit) approach is effective. An ablation study further demonstrates the importance of our design choice in joint training.
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