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Whether a Sudoku puzzle is easy or hard to solve depends on the complexity of the solution strategies that are required. However, complexity is probably not the only factor. When solving a Sudoku puzzle different strategies can be employed and are appropriate in different situations. We vary the instructions to bias participants to use different strategies and present subjects with situations where different strategies are appropriate. We test whether this has an effect on solution times.

This paper suggests that Herbert Simon’s concept of proof and predictions, in the solution of problems by human’s, considered as Information Processing Agents subject to boundedly rational behaviour and satisficing objectives, is to be interpreted in terms of constructive mathematics.

Human problem-solving is a fundamental yet complex phenomena; it has fascinated and attracted a lot of researchers to understand, and theorize about it. Modeling and simulating human problem-solving played a pivotal role in Herbert Simon’s research program. Herbert Simon (along with Allen Newell and Cliff Shaw) was among the pioneers of artificial intelligence, by interlinking cognitive psychology, economics, philosophy, and computer science through the research work on human problem-solving. His curiosity and work focused not on replicating the brain but to understand and replicate human problem solving techniques (which he often referred to as programs/algorithms). The main aim of this article is to trace the origins and paths that enabled Simon (Newell and Shaw) to further develop his (their) original ideas.

Herbert Simon and Allen Newell made important contributions to the study of human problem solving within an information processing system (IPS) framework. Contemporary debates and discussions on moral judgment and representation makes little or no reference to their work on problem-solving. This study argues that Simon and Newell’s IPS framework provides a useful integrative framework for the study of moral problem solving. Variations in the boundaries between the task environment and the IPS suggest its potential as a framework for a comparative study of intra and inter-species moral problem-solving.

Cognitive load theory is an instructional theory based on our knowledge of human cognitive architecture. Current versions of the theory use evolutionary educational psychology to categorise the information that humans process and to provide a template for human cognitive architecture that is analogous to the information processes of evolutionary biology. That theoretical base can be used to generate instructional procedures. The theoretical base and its attendant instructional effects provide information on the characteristics and role of human problem solving in instructional design. It is concluded that teachable aspects of skilled problem solving are domain-specific rather than generic-cognitive in nature, and that those characteristics should determine what is taught and how it is taught.

Transportation problem is a model which is commonly used in data structure solving a problem (human problem solving due to the computational method) because all the humans are related to transportation in any type of manner. Normally, traditional mathematical procedures used for solving the problem which is quite lengthy, after the computational solving procedures it comes to the bit easier to solve it except traditional lengthy methods. The Genetic Algorithm (GA) is most powerful tool for solving transportation problem. It refines the better optimal solution, for enhancing the optimization of transportation problem, using genetic algorithms lots of the work already has been done. This paper discusses the impact of genetic algorithms on two different types of systems environments i.e., Single-Processor Environment Systems and Multi-Processor Environment Systems, for solving the transportation problem and found the best optimal solution time of both systems. Index Terms— Transportation Problem, Genetics Algorithm (GA), Single-Processor Systems, Multi-Processor Systems, Optimization.

Significance The emerging field of gamified citizen science continually probes the fault line between human and artificial intelligence. A better understanding of citizen scientists’ search strategies may lead to cognitive insights and provide inspiration for algorithmic improvements. Our project remotely engages both the general public and experts in the real-time optimization of an experimental laboratory setting. In this citizen science project the game and data acquisition are designed as a social science experiment aimed at extracting the collective search behavior of the players. A further understanding of these human skills will be a crucial challenge in the coming years, as hybrid intelligence solutions are pursued in corporate and research environments., We introduce a remote interface to control and optimize the experimental production of Bose–Einstein condensates (BECs) and find improved solutions using two distinct implementations. First, a team of theoreticians used a remote version of their dressed chopped random basis optimization algorithm (RedCRAB), and second, a gamified interface allowed 600 citizen scientists from around the world to participate in real-time optimization. Quantitative studies of player search behavior demonstrated that they collectively engage in a combination of local and global searches. This form of multiagent adaptive search prevents premature convergence by the explorative behavior of low-performing players while high-performing players locally refine their solutions. In addition, many successful citizen science games have relied on a problem representation that directly engaged the visual or experiential intuition of the players. Here we demonstrate that citizen scientists can also be successful in an entirely abstract problem visualization. This is encouraging because a much wider range of challenges could potentially be opened to gamification in the future.

Sudoku is a widely popular NP-Complete combinatorial puzzle whose prospects for studying human computation have recently received attention, but the algorithmic hardness of Sudoku solving is yet largely unexplored. In this paper, we study the statistical mechanical properties of random Sudoku grids, showing that puzzles of varying sizes attain a hardness peak associated with a critical behavior in the constrainedness of random instances. In doing so, we provide the first description of a Sudoku freezing transition, showing that the fraction of backbone variables undergoes a phase transition as the density of pre-filled cells is calibrated. We also uncover a variety of critical phenomena in the applicability of Sudoku elimination strategies, providing explanations as to why puzzles become boring outside the typical range of clue densities adopted by Sudoku publishers. We further show that the constrainedness of Sudoku puzzles can be understood in terms of the informational (Shannon) entropy of their solutions, which only increases up to the critical point where variables become frozen. Our findings shed light on the nature of the k-coloring transition when the graph topology is fixed, and are an invitation to the study of phase transition phenomena in problems defined over alldifferent constraints. They also suggest advantages to studying the statistical mechanics of popular NP-Hard puzzles, which can both aid the design of hard instances and help understand the difficulty of human problem solving.

The only multiple-choice test of metaphoric comprehension presently available was constructed from figures of speech occurring in compositions written by children and young adults. Metaphoric competence was defined in terms of the various tests of figurative ability as well as in terms of figurative scorings for as many of the remaining tasks as possible. R. J. Sternberg’s analysis lays claim to some pretty fair historical ancestry in suggesting analogy as the major process underlying metaphoric use and/or understanding. The major point, however, is that a strict use of analogy as the model for metaphor may serve to impoverish metaphoric diction rather than enrich it. Researchers concerned with memory for metaphor, for example, have often made use of proverbs or Shakespearean metaphors. Other researchers, particularly those working with children, have had to construct their own evaluation procedures. The chapter aims to examine the pattern of relationships existing between complex problem solving and figurative competence.

In this paper, we consider learning by human beings and machines in the light of Herbert Simon’s pioneering contributions to the theory of Human Problem Solving. Using board games of perfect information as a paradigm, we explore differences in human and machine learning in complex strategic environments. In doing so, we contrast theories of learning in classical game theory with computational game theory proposed by Simon. Among theories that invoke computation, we make a further distinction between computable and computational or machine learning theories. We argue that the modern machine learning algorithms, although impressive in terms of their performance, do not necessarily shed enough light on human learning. Instead, they seem to take us further away from Simon’s lifelong quest to understand the mechanics of actual human behaviour.

The paper concerns an important but underappreciated genre of algorithmic puzzles, explaining what these puzzles are, reviewing milestones in their long history, and giving two different ways to classify them. Also covered are major applications of algorithmic puzzles in cognitive science research, with an emphasis on insight problem solving, and the advantages of algorithmic puzzles over some other classes of problems used in insight research. The author proposes adding algorithmic puzzles as a separate category of insight problems, suggests 12 specific puzzles that could be useful for research in insight problem solving, and outlines several experiments dealing with other cognitive aspects of solving algorithmic puzzles.

This article presents insights from a laboratory experiment on human problem solving in a combinatorial task. I rely on a hierarchical rugged landscape to explore how human problem-solvers are able to detect and exploit patterns in their search for an optimal solution. Empirical findings suggest that solvers do not engage only in local and random distant search, but as they accumulate information about the problem structure, solvers make ‘model-based’ moves, a type of cognitive search. I then calibrate an agent-based model of search to analyse and interpret the findings from the experimental setup and discuss implications for organizational search. Simulation results show that, for non-trivial problems, performance can be increased by a low level of persistence, that is, an increased likelihood to quickly abandon unsuccessful paths.

Both standard formulations of the frame problem and standard solutions implicitly assume that the re-identification of objects as persisting individuals between pre- and post-action contexts is unproblematic. In the case of human beings, this assumption is false: humans dedicate considerable cognitive resources to object re-identification. An analysis of both the phenomenology and neurocognitive implementation of object re-identification is used to show that in humans, all of the information architecturally available to solve the frame problem is in fact deployed for object re-identification. The frame problem is, therefore, equivalent to the object re-identification problem in the case of human problem solving.

This paper contributes to the understanding of human problem solving involved in mental tasks that require exploration among alternatives. Examples of such tasks are theorem proving and classical games like chess. De Groot’s largely used model of chess players’ thinking conceptually consists of two stages: (1) detection of general possibilities, or “motifs”, that indicate promising ideas the player may try to explore in a given chess position, and (2) calculation of concrete chess variations to establish whether any of the motifs can indeed be exploited to win the game. Strong chess players have to master both of these two components of chess problem solving skill. The first component reflects the player’s chess-specific knowledge, whereas the second applies more generally in game playing and other combinatorial problems. In this paper, we studied experimentally the relative importance of the two components of problem solving skill in tactical chess problems. A possibly surprising conclusion of our experiments is that for our type of chess problems, and players over a rather large range of chess strength, it is the calculating ability, rather than chess-specific pattern-based knowledge, that better discriminates among the players regarding their success. We also formulated De Groot’s model as a Causal Bayesian Network and set the probabilities in the network according to our experimental results.

PurposeDespite its popularity in practice, sales comparison is constantly criticized as subjective and even relegated to the least recommended method by some scholars. This paper aims to examine the validity of the above statement.Design/methodology/approachA number of statistical techniques have been proposed to improve the objectivity of the sales comparison approach. In contrast, property valuation can also be seen as an exercise of human problem solving with respect to market information. As a result, the approach of a controlled experiment is employed.FindingsFirst, experienced appraisers tend to adopt an appraisal process that significantly differs from that specified in legal standards. Second, appraisers have developed a specific‐to‐general information inquiry path. Third, appraisers are likely to stop examining additional sales evidence early when the appraised subject is a typical product. Fourth, appraisers have a tendency to weigh the comparables that come to their attention earlier more heavily than those that come later. Finally, despite the different strategies of information absorption, value variations among appraisers are consistent between different residential properties. The evidence, taken together, strongly suggests that professional appraisers have developed some heuristics or short‐cuts in digesting information relevant to appraisal.Originality/valueThis study is one of the very few that examine appraisers' decision making in Taiwan.

An interactive 3D desktop virtual environment task was created to investigate learning mechanisms in human problem solving. Participants were assessed for previous video game experience, divided into two groups (Training and Control), and matched for gender and experience. The Training group learned specific skills within the virtual environment before being presented a problem. The Control group was presented the problem only. Completion time was faster for the Training group and was affected by level of previous video game experience. Results indicated problem solving was a function of specific and general experience and demonstrated a method for dissociating these two facets of experience.

Acknowledging the importance of exercising a more stringent control over subcontractors, an influential industry report urges the establishment and maintenance of a Construction Subcontractor Registration (CSR) system. One key aspect of a credible and sustainable CSR system is to ensure that those companies seeking registration are assessed according to reliable standards and mechanisms. In practice, many managers rely on intuition, experience or predictive judgment to deal with the dynamic but interrelated requirements originating from various subcontractor and work categories. Being an artificial intelligence approach that mimics human problem solving skills, the case-based reasoning (CBR) approach should have potential in providing a flexible, transparent, objective and simple CSR system. This paper describes the development of a case-based CSR system. The results of this paper should improve our understanding on the process of CSR, evaluation criteria and information requirements. The case-based CSR model will also help formulate a more structured standard for the evaluation of subcontractors.

Psychological evidence suggests that humans use visual knowledge and reasoning in solving complex problems. We present Covlan, a visual knowledge representation language for representing visual knowledge and supporting visual reasoning. We describe Galatea, a computer program that uses Covlan for analogical transfer of problem-solving procedures from known analogs to new problems. We present the use of Galatea to model analogical visual problem solving by four human experimental participants, and describe one of the four cases in detail. The Galatea model of human problem solving suggests that problem-solving procedures can be effectively represented with Covlan.

Brothers and sisters have I none But this man's father is my father's son. If a man has only ten trees and wants to plant them in five rows with four trees to a row, how can he do it? 1, 2, 3, 5, 7, 11, –, – How did you solve these problems? What mental processes did you use? How can these processes be measured? With the advent of electronics, “thinking machines,” and information theory has come the possibility that human problem solving can now be studied in a more objective, quantitative, and systematic way.

International audience; Since the beginning of cognitive science, researchers have tried to understand human strategies in order to develop efficient and adequate computational methods. In the domain of problem solving, the Trav-eling Salesperson Problem has been used for the investigation and modeling of human solutions. We propose to extend this effort with an online game, in which instances of the Traveling Salesperson Problem have to be solved in the context of a game experience. We report on our effort to design and run such a game, present the data contained in the resulting openly available dataset, and provide an outlook on the use of games in general for cognitive science research. In addition, we present three geometrical models mapping the starting point preferences in the problems presented in the game as the result of an evaluation of the dataset.

The question of how humans solve problem has been addressed extensively. However, the direct study of the effectiveness of this process seems to be overlooked. In this paper, we address the issue of the effectiveness of human problem solving: we analyze where this effectiveness comes from and what cognitive mechanisms or heuristics are involved. Our results are based on the optimal probabilistic problem solving strategy that appeared in Solomonoff paper on general problem solving system. We provide arguments that a certain set of cognitive mechanisms or heuristics drive human problem solving in the similar manner as the optimal Solomonoff strategy. Specifically, we argue that there is a concrete mathematical background for the effectiveness of human problem solving, and we show how it is connected with several well established components of human cognition. The results presented in this paper can serve both cognitive psychology in better understanding of human problem solving processes as well as artificial intelligence in designing more human-like agents.

Pri inteligentnih tutorskih sistemih je pomembno, da sistem razume, kako težak je določen problem za učenca. Kako samodejno oceniti tovrstno težavnost, ostaja odprto vprašanje. Namen raziskave je razviti algoritmičen pristop k ugotavljanju težavnosti, ki bi ga lahko uporabljali pri avtomatiziranem ocenjevanju težavnosti problemov za človeka. Osredotočili se bomo na ocenjevanje težavnosti problemov, pri katerih težavnost izvira iz kombinatorične kompleksnosti in kjer je potrebno preiskovanje med alternativami. Pristop temelji na uporabi hevrističnega računalniškega preiskovanja za gradnjo preiskovalnih dreves, ki so ``smiselna'' z vidika osebe, ki problem rešuje. Pokazali bomo, da je s pomočjo analize lastnosti tovrstnih ``smiselnih'' dreves računalniški program sposoben napovedati, kako težak za reševanje je določen problem. Naš program je bil sposoben z visoko stopnjo natančnosti ločevati med enostavnimi in težkimi šahovskimi taktičnimi problemi. In intelligent tutoring systems, it is important for the system to understand how difficult a problem is for the student. However, it is an open question how to automatically assess such difficulty. The aim of our research is to find formalized measures of difficulty that could be used in automated assessment of the difficulty of a mental task for a human. We present a computational approach to estimating the difficulty of problems in which the difficulty arises from the combinatorial complexity of problems where a search among alternatives is required. Our approach is based on a computer heuristic search for building search trees that are “meaningful” from a human's point of view. We demonstrate that by analyzing properties of such trees, the program is capable to predict how difficult it would be for a human to solve the problem. In the experiments with chess tactical problems our program was able to differentiate between easy and difficult problems with a high level of accuracy.

Pri inteligentnih tutorskih sistemih je pomembno, da sistem razume, kako težak je določen problem za učenca. Kako samodejno oceniti tovrstno težavnost, ostaja odprto vprašanje. Namen raziskave je razviti algoritmičen pristop k ugotavljanju težavnosti, ki bi ga lahko uporabljali pri avtomatiziranem ocenjevanju težavnosti problemov za človeka. Osredotočili se bomo na ocenjevanje težavnosti problemov, pri katerih težavnost izvira iz kombinatorične kompleksnosti in kjer je potrebno preiskovanje med alternativami. Pristop temelji na uporabi hevrističnega računalniškega preiskovanja za gradnjo preiskovalnih dreves, ki so ``smiselna'' z vidika osebe, ki problem rešuje. Pokazali bomo, da je s pomočjo analize lastnosti tovrstnih ``smiselnih'' dreves računalniški program sposoben napovedati, kako težak za reševanje je določen problem. Naš program je bil sposoben z visoko stopnjo natančnosti ločevati med enostavnimi in težkimi šahovskimi taktičnimi problemi. In intelligent tutoring systems, it is important for the system to understand how difficult a problem is for the student. However, it is an open question how to automatically assess such difficulty. The aim of our research is to find formalized measures of difficulty that could be used in automated assessment of the difficulty of a mental task for a human. We present a computational approach to estimating the difficulty of problems in which the difficulty arises from the combinatorial complexity of problems where a search among alternatives is required. Our approach is based on a computer heuristic search for building search trees that are “meaningful” from a human's point of view. We demonstrate that by analyzing properties of such trees, the program is capable to predict how difficult it would be for a human to solve the problem. In the experiments with chess tactical problems our program was able to differentiate between easy and difficult problems with a high level of accuracy.

Serious Games research has become an active research topic in the recent years. In order to design Serious Games with an appropriate degree of complexity such that the games are neither boring nor frustrating, it is necessary to have a good understanding of the factors that determine the difficulty of a game. The present work is based on the idea that a game’s difficulty is reflected in the structure of its underlying state space. Therefore, we propose metrics to capture the structure of a state space and examine if their values correlate with the difficulty of the game. However, we find that only one of the metrics, namely the length of the optimal solution, influences the difficulty of the game. In addition, by focusing on the part of the state space, which is actually explored by human players, we can identify properties that predict the game’s difficulty perceived by the players. We thus conclude that it is not the structure of the whole state space that determines the difficulty of a game, but the rather limited part that is explored by human players.

This essay traces two research programmes in broad strokes. Both programmes start from the same observation — the behaviour of an ant (or termite) colony and the ability of the ant colony to act in a collective manner to achieve goals that the individual ant cannot. For one programme such behaviour is indicative of intelligence; for the other it is indicative of (collective) instinct. The primary intention of the essay is not to assess the claims of intelligence found, but to consider the rationale of the researchers involved in the two programmes for doing such research. It is observed that virtue in one programme is understanding (with the concomitant ability to explain — and, hence, teach), while the primary virtue in the other programme is the utility — and ultimately efficiency — that this may add to human problem solving skills. The two programmes used as illustration are Eugène Marais’s study of termites in the first half of the 20th century and the emergence of artificial intelligence projects that are inspired by ant behaviour in the second half of the 20th century. The essay suggests that the current emphasis of inquiry at tertiary education institutions embraces utility to the extent that it displaces pure insight — and hence the ability to explain and, ultimately, the ability to teach.

Technical systems evolve from simple dedicated task solvers to cooperative and competent assistants, helping the user with increasingly complex and demanding tasks. For this, they may proactively take over some of the users responsibilities and help to find or reach a solution for the user’s task at hand, using e.g., Artificial Intelligence (AI) Planning techniques. However, this intertwining of user-centered dialog and AI planning systems, often called mixed-initiative planning (MIP), does not only facilitate more intelligent and competent systems, but does also raise new questions related to the alignment of AI and human problem solving. In this paper, we describe our approach on integrating AI Planning techniques into a dialog system, explain reasons and effects of arising problems, and provide at the same time our solutions resulting in a coherent, userfriendly and efficient mixed-initiative system. Finally, we evaluate our MIP system and provide remarks on the use of explanations in MIP-related phenomena.

This paper describes Eureka, a problem-solving architecture that operates under strong constraints on its memory and processes. Most significantly, Eureka does not assume free access to its entire long-term memory. That is, failures in problem solving may arise not only from missing knowledge, but from the (possibly temporary) inability to retrieve appropriate existing knowledge from memory. Additionally, the architecture does not include systematic backtracking to recover from fruitless search paths. These constraints significantly impact Eureka's design. Humans are also subject to such constraints, but are able to overcome them to solve problems effectively. In Eureka's design, we have attempted to minimize the number of additional architectural commitments, while staying faithful to the memory constraints. Even under such minimal commitments, Eureka provides a qualitative account of the primary types of learning reported in the literature on human problem solving. Further commitments to the architecture would refine the details in the model, but the approach we have taken de-emphasizes highly detailed modeling to get at general root causes of the observed regularities. Making minimal additional commitments to Eureka's design strengthens the case that many regularities in human learning and problem solving are entailments of the need to handle imperfect memory.

Visual analogy is believed to be important in human problem solving. Yet, there are few computational models of visual analogy. In this paper, we present a preliminary computational model of visual analogy in problem solving. The model is instantiated in a computer program, called Galatea, which uses a language for representing and transferring visual information called Privlan. We describe how the computational model can account for a small slice of a cognitive-historical analysis of Maxwell’s reasoning about electromagnetism.

Artificial neural networks (ANN) represent one of the fastest developing fields of artificial intelligence due to their ability to resemble (to a certain extent) the human problem solving characteristic which is difficult to simulate using the logical, analytical techniques of expert system and standard software technologies. As catalysis plays a key role in green chemistry, the applications of ANN in this area are of great interest since crucial problems such as catalyst formulation, optimal conditions for catalytic systems exploitation and other specific problems can be rapidly and efficiently solved. The aim of this review is to evaluate the state-of-the art of ANN in catalysis and to point out further developments in this field.

This editorial introduction first outlines some of the research challenges raised by the emerging forms of internet-scale human problem solving. It then explains how the two articles in this special section can serve as illuminating complementary case studies, providing concrete examples embedded in general conceptual frameworks.

The application of knowledge is fundamental to human problem solving. In health disciplines, knowledge utilization commonly manifests through evidence-based decision making in practice. The purpose of this paper is to explore the development of the evidence-based practice (EBP) movement in health professions in general, and dental hygiene in particular, and to examine its relationship to the professionalization agenda of dental hygiene in Canada. EBP means integrating practitioner expertise with the best available external evidence from research. Proponents of EBP believe that it holds promise for reducing a research–practice gap by encouraging clinicians to seek current research results. Both the Canadian and American Dental Hygienists Associations support practice based on current research evidence, yet recent studies show variation in practice. Professionalization refers to the developmental stages through which an organized occupation passes as it develops traits that characterize it as a profession. The status conferred by professionalization privileges a group to make and monitor its own decisions relative to practice. Dental hygiene's success in acquiring attributes of a profession suggests that transformation to a profession is occurring. This paper compares the assumptions and challenges of both movements, and argues the need for a principal focus on the development of a culture of evidence-based dental hygiene practice.