Plans, goals and selection rules in the comprehension of computer programs

The notion of the programming plan has been proposed as a mechanism through which one can explain the nature of expertise in programming. Soloway and Ehrlich (1984) suggest that such expertise is characterized by the existence and use of programming plans. However, studies in other complex problem-solving domains, notably text editing, suggest that expertise is characterized not only by the possession of plan-related structures but also by the development of appropriate selection rules which govern the implementation of plans in appropriate situations (Card et al. 1980, Kay and Black 1984, 1986). This paper presents an experimental study which examines the role of programming plans in the context of skill development in programming. The results of this study suggest that plan-based structures cannot be used in isolation to explain novice/expert differences. Indeed, such structures appear to prevail at intermediate levels of skill. The major characteristic of expertise in programming would appear to be strongly related to the development of appropriate selection rules and to so-called program discourse rules. This in turn suggests that current views on the role of plan-based structures in expert programming performance are too limited in their conception to provide an adequate basis for a thorough analysis of the problem-solving activity in the programming domain.     

Learning new programming languages: an analysis of the process and problems encountered

Experienced programmers transferring to a new language have a far easier time than the novice learning a first language. However, they still experience considerable difficulties. The objective in this exploratory study was to characterize the kinds of learning and transfer that take place in the early stages of using a new programming language and where difficulties develop. 'Think-aloud' protocols were videotaped as subjects went about trying to write a program in a new programming language. Subjects used One of two unfamiliar languages, one similar to their known language (Pascal) and the other dissimilar. Three types of analyses were done on the recorded protocols: a procedural analysis showing the activities the subjects engaged in as they learned the new language, a programming knowledge analysis showing in which areas of program development difficulties were encountered, and a solutions analysis showing how successful the programmers were at using unique features of the new language. We found that the procedures programmers used to learn a new language were independent of the language being learned. The slight differences that existed in procedures were between levels of expertise. Programmers spent the majority of their time reading a language textbook. The programming knowledge analysis showed that programmers main area of concentration was planning how to implement their approach given the constructs available in the language. We observed many iterations of programmers trying to implement plans, failing and having to revise their plans. Examination of the subjects solutions and implementation approaches in Pascal led us to believe that programmers learning a new language are often biased by their implementation of algorithms in previous languages.     

Learning new programming languages: an analysis of the process and problems encountered

Abstract

Experienced programmers transferring to a new language have a far easier time than the novice learning a first language. However, they still experience considerable difficulties. The objective in this exploratory study was to characterize the kinds of learning and transfer that take place in the early stages of using a new programming language and where difficulties develop. ‘Think-aloud’ protocols were videotaped as subjects went about trying to write a program in a new programming language. Subjects used One of two unfamiliar languages, one similar to their known language (Pascal) and the other dissimilar. Three types of analyses were done on the recorded protocols: a procedural analysis showing the activities the subjects engaged in as they learned the new language, a programming knowledge analysis showing in which areas of program development difficulties were encountered, and a solutions analysis showing how successful the programmers were at using unique features of the new language. We found that the procedures programmers used to learn a new language were independent of the language being learned. The slight differences that existed in procedures were between levels of expertise. Programmers spent the majority of their time reading a language textbook. The programming knowledge analysis showed that programmers main area of concentration was planning how to implement their approach given the constructs available in the language. We observed many iterations of programmers trying to implement plans, failing and having to revise their plans. Examination of the subjects solutions and implementation approaches in Pascal led us to believe that programmers learning a new language are often biased by their implementation of algorithms in previous languages.     

Method for solving multiobjective aggregate production planning problem with fuzzy parameters

We propose an efficient method that transforms a fuzzy multiple objective linear programming (MOLP) problem model to crisp MOLP model, and an interactive solution procedure that suggest the best compromise aggregate production plans for the multi-period fuzzy multiple objective aggregate production planning (APP) problem.     

2APL: a practical agent programming language

This article presents a BDI-based agent-oriented programming language, called 2APL (A Practical Agent Programming Language). This programming language facilitates the implementation of multi-agent systems consisting of individual agents that may share and access external environments. It realizes an effective integration of declarative and imperative style programming by introducing and integrating declarative beliefs and goals with events and plans. It also provides practical programming constructs to allow the generation, repair, and (different modes of) execution of plans based on beliefs, goals, and events. The formal syntax and semantics of the programming language are given and its relation with existing BDI-based agent-oriented programming languages is discussed.     

Program families: Program construction by context independent refinements

The concept of program families is a generalisation of the conventional stepwise refinement paradigm. We formalise program families by allowing Hoare-triplets to be parameterized. Next we derive a simple calculus to develop programs which are known a priori to be correct with respect to explicitly formulated pre- and postconditions.

Program families deal with at least two important problems of conventional refinement steps, i.e. program families are not context dependent and they apply just as well to top-down decomposition as to the bottom-up or middle-out approach. It turns out that the meaning of a pseudostatement in the context of program families is quite different from its meaning in the conventional refinement process.

A couple of examples illustrate the technique: the 1000 primes problem, a palindrome filter and a sorting routine.

The discussion relates program families to Morgan's refinement calculus, Knuth' literate programming and Soloway's programming plans.     

Multiple multimodal mentors: delivering computer-based instruction via specialized anthropomorphic advisors

We describe the Smalltalk Gurus, components of the MoleHill intelligent tutoring system for Smalltalk programming. The Gurus offer help on plans for achieving goals in the Smalltalk environment, as well as remediation for students' incorrect and less-than-optimal plans. The Gurus' assistance is provided via the multimodal media of animation and voice-over audio. MoleHill employs multiple Gurus to deliver advice and instruction concerning disparate information domains, thus facilitating learners' cognitive organization and assimilation of new knowledge and information. We have labelled the approach instantiated by the Smalltalk Gurus the guru instructional model, one which is generally applicable to computer-based advisory systems.     

最优递阶随机生产计划与控制

研究了敏捷制造车间(AMW)中的最优递阶随机生产计划与控制问题.首先根据实际 需要建立关联方程有延迟的车间生产的随机非线性规划模型,即一种求解动态优化问题的静态 优化模型.为求解方便将其转化成确定非线性规划模型并通过引进约束进一步转化成线性规划 模型.然后,提出分别用卡马卡算法和基于卡马卡算法的关联预测法进行求解,并编制了相应软 件.算例研究表明所提方法是非常有效的.     

Real-time path planning for long-term information gathering with an aerial glider

Autonomous thermal soaring offers an opportunity to extend the flight duration of unmanned aerial vehicles (UAVs). In this work, we introduce the informative soaring problem, where a gliding UAV performs an information gathering mission while simultaneously replenishing energy from known thermal energy sources. We pose this problem in a way that combines convex optimisation with graph search and present four path planning algorithms with complementary characteristics. Using a target-search task as a motivating example, finite-horizon and Monte Carlo tree search methods are shown to be appropriate for situations with little prior knowledge, but suffer from either myopic planning or high computation cost in more complex scenarios. These issues are addressed by two novel tree search algorithms based on creating clusters that associate high uncertainty regions with nearby thermals. The cluster subproblems are solved independently to generate local plans, which are then linked together. Numerical simulations show that these methods find high-quality nonmyopic plans quickly. The more promising cluster-based method, which uses dynamic programming to compute a total ordering over clusters, is demonstrated in hardware tests on a UAV. Fifteen-minute plans are generated in less than four seconds, facilitating online replanning when simulated thermals are added or removed in-flight.     

Pair programming and the mysterious role of the navigator

Computer programming is generally understood to be highly challenging and since its inception a wide range of approaches, tools and methodologies have been developed to assist in managing its complexity. Relatively recently the potential benefits of collaborative software development have been formalised in the practice of pair programming. Here we attempt to ‘unpick’ the pair programming process through the analysis of verbalisations from a number of commercial studies. We focus particularly on the roles of the two programmers and what their key characteristics and behaviours might be. In particular, we dispute two existing claims: (i) that the programmer who is not currently typing in code (“the navigator”) is constantly reviewing what is typed and highlighting any errors (i.e. acting as a reviewer) and (ii) that the navigator focuses on a different level of abstraction as a way of ensuring coverage at all necessary levels (i.e. acting as a foreman). We provide an alternative model for these roles (“the tag team”) in which the driver and navigator play much more equal roles. We also suggest that a key factor in the success of pair programming may be the associated increase in talk at an intermediate level of abstraction.     

Interaction styles and input/output devices

Abstract

We describe the Smalltalk Gurus, components of the MoleHill intelligent tutoring system for Smalltalk programming. The Gurus offer help on plans for achieving goals in the Smalltalk environment, as well as remediation for students' incorrect and less-than-optimal plans. The Gurus' assistance is provided via the multimodal media of animation and voice-over audio. MoleHill employs multiple Gurus to deliver advice and instruction concerning disparate information domains, thus facilitating learners' cognitive organization and assimilation of new knowledge and information. We have labelled the approach instantiated by the Smalltalk Gurus the guru instructional model, one which is generally applicable to computer-based advisory systems.     

Bi-level programming enabled design of an intelligent maritime search and rescue system

This paper studies an intelligent maritime search and rescue (SAR) system problem. According to historical accidents and available SAR equipment information, a bi-level mixed-integer programming (MIP) model is proposed to determine the type and number of SAR equipment allocated to activated stations. Particle swarm optimization (PSO) algorithm and genetic algorithm (GA) algorithm are applied to solve the proposed mathematical model. Computational experiments based on real instances in the East Sea China not only validate the effectiveness of the bi-level MIP model in balancing two objectives during decision process, but also indicate that PSO algorithm is better than GA algorithm to solve the proposed model and generate reasonable equipment allocation plans. Some managerial implications are also outlined on the basis of the numerical experiments.     

Plan-based delivery composition in intelligent tutoring systems for introductory computer programming

In a shell system for the generation of intelligent tutoring systems, the instructional model that one applies should be variable independent of the content of instruction. In this article, a taxonomy of content elements is presented in order to define a relatively content-independent instructional planner for introductory programming ITS's; the taxonomy is based on the concepts of programming goals and programming plans. Deliveries may be composed by the instantiation of delivery templates with the content elements. Examples from two different instructional models illustrate the flexibility of this approach. All content in the examples is taken from a course in COMAL-80 turtle graphics.     

Programming Rational Agents in a Modal Action Logic

In this paper we describe a language for reasoning about actions that can be used for modelling and for programming rational agents. We propose a modal approach for reasoning about dynamic domains in a logic programming setting. Agent behavior is specified by means of complex actions which are defined using modal inclusion axioms. The language is able to handle knowledge producing actions as well as actions which remove information. The problem of reasoning about complex actions with incomplete knowledge is tackled and the temporal projection and planning problems is addressed; more specifically, a goal directed proof procedure is defined, which allows agents to reason about complex actions and to generate conditional plans. We give a non-monotonic solution for the frame problem by making use of persistency assumptions in the context of an abductive characterization. The language has been used for implementing an adaptive web-based system.     

Reasoning about plan revision in BDI agent programs

Facilities for handling plan execution failures are essential for agents which must cope with the effects of nondeterministic actions, and some form of failure handling can be found in most mature agent programming languages and platforms. While such features simplify the development of more robust agents, they make it hard to reason about the execution of agent programs, e.g., to verify their correctness. In this paper, we present an approach to the verification of agent programs which admit exceptional executions. We consider executions of the BDI-based agent programming language 3APL in which plans containing non-executable actions can be revised using plan revision rules, and present a logic for reasoning about normal and exceptional executions of 3APL programs. We provide a complete axiomatization for the logic and, using a simple example, show how to express properties of 3APL programs as formulas of the logic.     

布雷方案评估的灰关联度方法

针对布雷作战方案优选问题,提出了一种新的评估方法。该方法定义各可行方案与理想、负理想方案的灰关联系数,通过最大最小和最小最大的双目标优化模型客观地得到属性的权重向量,根据各方案的接近度,给出布雷方案评估方法。仿真实例验证了方法的有效性。     

Travel Plans in Public Transit Networks Using Artificial Intelligence Planning Models

Users of public transit networks require tools that generate travel plans to traverse them. The main issue is that public transit networks are time and space dependent. Travel plans depend on the current location of users and transit units, along with a set of user preferences and time restrictions. In this work, we propose the design and development of artificial intelligence (AI) planning models for engineering travel plans for such networks. The proposed models consider temporal actions, bus locations, and user preferences as constraints, to restrict the set of travel plans generated. Our approach decouples model design from algorithm construction, providing a greater level of flexibility and richness of solutions. We also introduce an integer linear programming formulation, and a fast preprocessing procedure, to evaluate the quality of the solutions returned by the proposed planning models. Experimental results show that AI planning models can efficiently generate close to optimal solutions. Furthermore, our analysis identifies user preferences as the most critical factor that increases solution complexity for planning models.     

用Excel修正流体阻力摩擦系数关联式的探讨

本文应用Excel的函数库和图表向导功能,对莫狄图中完全湍流区域的摩擦系数的求取公式做一补充.即给出图中虚线的拟合公式.将此公式作为流体活动区域变换的判据,能够使计算机编程体系更加完备.