Agent coordination and communication in sociotechnological systems: Design and measurement issues

This article is concerned with enhancing agent coordination in modern sociotechnological systems. To this end, sociotechnological systems are conceptualized as problem solving systems that comprise human and technological agents engaged in dynamic collaboration. Following this, there is a discussion of the challenge of achieving agent coordination in problem solving systems as technological agents become increasingly autonomous. A key assertion is that agent coordination in problem solving systems might be enhanced through the study of competent coordination in living systems such as human and animal groups. Based on a review of research on competent coordination in human and animal groups, design principles for problem solving systems are then presented. Finally, methods are proposed for measuring the extent to which a given agent operates in accordance with these principles.     

一种双向求解平行结构类问题的方法

平行结构类问题是一类适于分布式求解的人工智能问题．已有的大多数求解方法均采用预期或目标来指导自底向上的问题求解．但这些预期或目标是以局部问题求解状态为基础的，指导性较弱．尽管有的方法（如改进的ＤＶＭＴ结构）允许高层了解，但未给出明确的求解算法．本文提出一种双向求解平行结构类问题的方法，首先根据全局问题求解状态生成预期，指导自底向上的求解，然后根据新产生的假设来验证和修改预期，并用新的预期重新指导求解．该方法不仅提高了预期的指导性，而且使问题求解更为灵活．     

Perspectives on problem solving and instruction

Most educators claim that problem solving is important, but they take very different perspective on it and there is little agreement on how it should be taught. This article aims to sort out the different perspectives and discusses problem solving as a goal, a method, and a skill. As a goal, problem solving should not be limited to well-structured problem solving but be extended to real-life problem solving. As a method, problem solving has clear limitations for novice learners; providing ample support to learners is of utmost importance for helping them to develop problem-solving skills. As a skill, problem solving should not be seen as something that only occurs in the early phases of a process of expertise development but as a process that develops in parallel in System 1 and System 2. The four-component instructional design model (4C/ID) is briefly discussed as an approach that is fully consistent with the conceptualization described in this article and as a preliminary answer to the question how problem solving is best taught.     

The effect of simulation games on the learning of computational problem solving

Simulation games are now increasingly applied to many subject domains as they allow students to engage in discovery processes, and may facilitate a flow learning experience. However, the relationship between learning experiences and problem solving strategies in simulation games still remains unclear in the literature. This study, thus, analyzed the feedback and problem solving behaviors of 117 students in a simulation game, designed to assist them to learn computational problem solving. It was found that students when learning computational problem solving with the game were more likely to perceive a flow learning experience than in traditional lectures. The students’ intrinsic motivation was also enhanced when they learned with the simulation game. In particular, the results of the study found a close association between the students’ learning experience states and their problem solving strategies. The students who perceived a flow experience state frequently applied trial-and-error, learning-by-example, and analytical reasoning strategies to learn the computational problem solving skills. However, a certain portion of students who experienced states of boredom and anxiety did not demonstrate in-depth problem solving strategies. For instance, the students who felt anxious in the simulation game did not apply the learning-by-example strategy as frequently as those in the flow state. In addition, the students who felt bored in the simulation game only learned to solve the problem at a superficial level.     

MorphoTRIZ – Solving Technical Problems with a Demand for Multi‐Smart Solutions

Using two approved creativity techniques, technical problem solving can be supported in a systematic and analytical way: morphological and contradiction‐oriented problem solving (the latter as part of the Theory of Inventive Problem Solving [Russian acronym: TRIZ]). In these techniques different anchor points for creativity are used: morphological‐oriented problem solving is characterized by structuring a system into independent partial systems and finding answers by combining the different solutions of these subsystems; contradiction‐oriented problem solving is focused on finding fundamental contradictions within a system, representing the core problems, and solving them by applying the accumulated experiential knowledge of previous inventors. In this paper a combination of both techniques is developed and deployed, using network‐oriented function analysis as a connecting concept. We refer to this combination as MorphoTRIZ. It is particularly helpful in technical problem solving, when there is a demand for a multitude of smart solutions, which has become quite common with regard to design‐oriented products and individualized mass production as well as various other purposes. Seen from the conceptional perspective of creative problem solving, MorphoTRIZ combines process elements: (i) for producing a multitude of ideas, (ii) for producing many categories of ideas, and (iii) for producing highly original ideas. It provides connections to further creativity techniques, to open innovation and also to the field of management research as application area.     

Teaching Thinking and Problem Solving at University: A Course on TRIZ

Thinking and problem solving skills are considered to be of significant importance in many professions. Surveys indicate that university education fails in appropriately enhancing these skills. This paper presents a concept of teaching thinking and problem solving as a separate course, based on the Theory of Inventive Problem Solving (TRIZ). Student surveys showed that students' perception of their abilities in problem solving changed vastly as a consequence of the course. Students reflected that they would never have expected themselves to come up with the ideas they eventually thought of and suggested while conducting their final project, had they not been formally taught about the tools of problem solving. It was also found that this course on TRIZ thinking tools impacted students' problem solving abilities much more than discipline‐based courses, supporting the superiority of the ‘enrichment’ over the ‘infusion’ approach.     

Wolves, bees, and football: Enhancing coordination in sociotechnological problem solving systems through the study of human and animal groups

This paper describes how sociotechnological systems comprising human and technological agents can be considered problem solving systems. Problem solving systems typically comprise many agents, each characterized by at least partial autonomy. A challenge for problem solving systems is to coordinate system agent operations during problem solving. This paper explores how competence models of human–human and animal–animal coordination might be used to inform the design of problem solving systems so that the potential for agent coordination is enhanced. System design principles are identified based on a review of competent coordination in human groups, such as work and sport teams, and animal groups, such wolf packs and bee colonies. These principles are then discussed in relation to agent coordination in the domains of E-Science, future combat systems, and medicine, which typify real-world environments comprising problem solving systems.     

因素空间与可拓学的互补性分析及问题处理融合模型

问题智能处理是人工智能领域具有挑战性和交叉性的课题。大数据与人工智能技术背景下,为了给具有较高普遍性的开放性问题求解提供新的理论支撑,对因素空间与可拓学做了对比分析,发现两者具有共同的数学基础,在问题智能处理等研究方向有交叉性和互补性。以面向问题处理的融合模型为例,论证了可拓学与因素空间交叉研究的基本路径及其可行性,提出了两者交叉研究的主要方向,阐明了实际应用的潜在优势及适用范围。该研究将有助于进一步提高问题处理的系统性、智能化水平,提高解决问题从不确定性、偶然性到必然性的程度,并将推动为智能科学服务的智能数学的发展。     

Perceiving and planning before acting: An approach to enhance global network coherence

Globally coherent behavior is essential for a distributed problem solving network. It is a characteristic of the whole problem solving process. We discuss in this article different forms of cooperation at different phases of the problem solving process, which have to be considered to increase global coherence. the connection problem and the timing problem are key issues for distributed problem solving. the “perceive-plan-act” loop is introduced for each problem solving node. It means that a node has to perceive the network state and plan its near-future activities before it takes an action. Different approaches to network perception are discussed. the experimental results show significant improvement of system performance.     

Diagrammatic Reasoning: An Artificial Intelligence Perspective

A common motivation for developing computationalframeworks for diagrammatic reasoning is the hope thatthey might serve as re-configurable tools for studyinghuman problem solving performance. Despite the ongoingdebate as to the precise mechanisms by which diagrams,or any other external representation, are used inhuman problem solving, there is little doubt thatdiagrammatic representations considerably help humanssolve certain classes of problems. In fact, there area host of applications of diagrams and diagrammaticrepresentations in computing, from data presentationto visual programming languages. In contrast to boththe use of diagrams in human problem solving and theubiquitous use of diagrams in the computing industry,the topic of this review is the use of diagrammaticrepresentations in automated problem solving. Wetherefore investigate the common, and often implicit,assumption that if diagrams are so useful for humanproblem solving and are so apparent in humanendeavour, then there must be analogous computationaldevices of similar utility.     

Global optimal solutions to general sensor network localization problem

Sensor network localization problem is to determine the position of the sensor nodes in a network given pairwise distance measurements. Such problem can be formulated as a quartic polynomial minimization via the least squares method. This paper presents a canonical duality theory for solving this challenging problem. It is shown that the nonconvex minimization problem can be reformulated as a concave maximization dual problem over a convex set in a symmetrical matrix space, and hence can be solved efficiently by combining a general (linear or quadratic) perturbation technique with existing optimization techniques. Applications are illustrated by solving some relatively large-scale problems. Our results show that the general sensor network localization problem is not NP-hard unless its canonical dual problem has no solution in its positive definite domain. Fundamental ideas for solving general NP-hard problems are discussed.     

Distinct and combined effects of disciplinary composition and methodological support on problem solving in groups

Several reasons for the use of multidisciplinary teams composed of individuals with natural science and engineering background in problem‐solving processes exist. The most important are the integration of science‐based technologies into products and processes, and benefits for the problem‐solving process thanks to new knowledge and new perspectives on problems. In this study we analyse the implications of interdisciplinary (science – engineering) group problem solving from a managerial as well as from a cognitive perspective. We then report on an experiment investigating the impact of problem‐relevant disciplinary group composition and methodological support on the problem‐solving process and its outcome. The findings of the experiment have managerial, theoretical, and pedagogical implications related to early phases of New Product/Process Design processes in high‐technology and scientific knowledge‐related domains.     

Computing near-optimal solutions for the dominating subset with minimal weight problem

In this work, we consider a combinatorial “dominating subset with minimal weight” problem, which is an associative problem for solving global optimization problem. This problem can be expressed as a kind of assignment problem. The mathematical model and the economical interpretations of the problem are given and its properties are described. Then, we propose a new algorithm which has a ratio bound in polynomial time, by using above properties for solving the problem and present the results of numerical experiments.     

The impact of goal specificity and goal type on learning outcome and cognitive load

Two hundred and thirty three 15-year old students conducted experiments within a computer-based learning environment. They were provided with different goals according to an experimental 2 × 2 design with goal specificity (nonspecific goals versus specific goals) and goal type (problem solving goals versus learning goals) as factors. We replicated the findings of other researchers that nonspecific problem solving goals lead to lower cognitive load and better learning than specific problem solving goals. For learning goals, however, we observed this goal specificity effect only on cognitive load but not on learning outcome. Results indicate that the goal specificity affects the element interactivity of a task and cognitive load with both, problem solving goals or learning goals. But differences in overall cognitive load are not sufficient for explaining differences in learning outcome. Additionally, differences in strategy use come into play. Specific problem solving goals seem to restrict students to use a problem solving strategy whereas nonspecific problem solving goals or learning goals allow students to use a learning strategy. We conclude that in order to foster learning, students must be provided with goals that allow them to use a learning strategy. Additionally, providing them with nonspecific goals decreases cognitive load and, thus, enables students to learn with less effort.     

A flowchart‐based intelligent tutoring system for improving problem‐solving skills of novice programmers

Intelligent tutoring and personalization are considered as the two most important factors in the research of learning systems and environments. An effective tool that can be used to improve problem‐solving ability is an Intelligent Tutoring System which is capable of mimicking a human tutor's actions in implementing a one‐to‐one personalized and adaptive teaching. In this paper, a novel Flowchart‐based Intelligent Tutoring System (FITS) is proposed benefiting from Bayesian networks for the process of decision making so as to aid students in problem‐solving activities and learning computer programming. FITS not only takes full advantage of Bayesian networks, but also benefits from a multi‐agent system using an automatic text‐to‐flowchart conversion approach for engaging novice programmers in flowchart development with the aim of improving their problem‐solving skills. In the end, in order to investigate the efficacy of FITS in problem‐solving ability acquisition, a quasi‐experimental design was adopted by this research. According to the results, students in the FITS group experienced better improvement in their problem‐solving abilities than those in the control group. Moreover, with regard to the improvement of a user's problem‐solving ability, FITS has shown to be considerably effective for students with different levels of prior knowledge, especially for those with a lower level of prior knowledge.     

Factors of problem-solving competency in a virtual chemistry environment: The role of metacognitive knowledge about strategies

The ability to solve complex scientific problems is regarded as one of the key competencies in science education. Until now, research on problem solving focused on the relationship between analytical and complex problem solving, but rarely took into account the structure of problem-solving processes and metacognitive aspects. This paper, therefore, presents a theoretical framework, which describes the relationship between the components of problem solving and strategy knowledge.In order to assess the constructs, we developed a virtual environment which allows students to solve interactive and static problems. 162 students of grade 10 and the upper secondary level completed the tests within a cross-sectional survey. In order to investigate the structure of problem-solving competency, we established measurement models representing different theoretical assumptions, and evaluated model fit statistics by using confirmatory factor analyses.Results show that problem-solving competency in virtual environments comprises to three correlated abilities: achieving a goal state, systematical handling of variables, and solving analytical tasks. Furthermore, our study provides empirical evidence on the distinction between analytical and complex problem solving.Additionally, we found significant differences between students of grades 10 and 12 within the problem-solving subscales, which could be explained by gaming experience and prior knowledge. These findings are discussed from a measurement perspective. Implications for assessing complex problem solving are given.     

Explanation-based learning:A problem solving perspective

This article outlines explanation-based learning (EBL) and its role in improving problem solving performance through experience. Unlike inductive systems, which learn by abstracting common properties from multiple examples, EBL systems explain why a particular example is an instance of a concept. The explanations are then converted into operational recognition rules. In essence, the EBL approach is analytical and knowledge-intensive, whereas inductive methods are empirical and knowledge-poor. This article focuses on extensions of the basic EBL method and their integration with the problem solving system. 's EBL method is specifically designed to acquire search control rules that are effective in reducing total search time for complex task domains. Domain-specific search control rules are learned from successful problem solving decisions, costly failures, and unforeseen goal interactions. The ability to specify multiple learning strategies in a declarative manner enables EBL to serve as a general technique for performance improvement. 's EBL method is analyzed, illustrated with several examples and performance results, and compared with other methods for integrating EBL and problem solving.     

The general problem solving algorithm and its implementation

By generalising problem solving techniques such as divide-and-conquer, dynamic programming, tree and graph searching, integer programming and branch-and-bound, a general problem solving algorithm is deduced. Various examples of the use of this algorithm are given and its implementation on both sequential and parallel machines, such as the cosmic cube, is discussed.