A model of human performance on the traveling salesperson problem

A computational model is proposed of how humans solve the traveling salesperson problem (TSP). Tests of the model are reported, using human performance measures from a variety of 10-, 20-, 40-, and 60-node problems, a single 48-node problem, and a single 100-node problem. The model provided a range of solutions that approximated the range of human solutions and conformed closely to quantitative and qualitative characteristics of human performance. The minimum path lengths of subjects and model deviated by average absolute values of 0.0%, 0.9%, 2.4%, 1.4%, 3.5%, and 0.02% for the 10-, 20-, 40-, 48-, 60-, and 100-node problems, respectively. Because the model produces a range of solutions, rather than a single solution, it may find better solutions than some conventional heuristic algorithms for solving TSPs, and comparative results are reported that support this suggestion.     

A production system model for human problem solving

Summary A new production system model was developed for a class of transformation problems, based upon the results of an experiment which evaluated the psychological validity of a noticing order in problem solving. This model specifies how general problem solving heuristics interact with domain knowledge and how possible actions are assembled from externally presented information. The production system may conceptually be divided into three groups: move generation, move evaluation, and move execution productions. The simulation model was evaluated by a second experiment. With a common set of parameter values, good fits were obtained between the predicted and observed data. The significance of individual productions was assessed by running simulations with individual productions deleted. While previous simulations have described subjects' behavior on a single problem, the present model successfully predicted human problem solving in three structurally different problems. The proposed production system thus gives a detailed account of the procedural knowledge that subjects use in transformation problems.     

Sequential decision making: An aggregate scheduling methodology

Recent studies of sequential decision making performance have utilized optimal dynamic programming solutions as the criteria for evaluating the quality of human decisions. The difficulties incumbent in increasing the dimensionality of dynamic programming formulations effectively prohibit their extension beyond the case of a single decision variable. In the following article an alternative optimization formulation for a two-decision-variable case is presented, followed by the development of a corresponding one-decision-variable problem. In addition, related methodological problems limiting the comparability of results among previous studies are presented. It is suggested that the proposed problem formulation and its accompanying interpretation for the decision maker largely eliminate these previous methodological problems. Furthermore, the problem formulation permits quantification of the relative weights that should optimally be placed on future decision periods in the multiple-stage decision problem.     

Domestic dogs (Canis familiaris) flexibly adjust their human-directed behavior to the actions of their human partners in a problem situation

Domestic dogs (Canis familiaris) have been shown to actively initiate triadic communicative interactions by looking at a human partner or by alternating their gaze between the human and an object when being faced with an out-of-reach reward or an unsolvable problem. It has hardly been investigated, however, whether dogs flexibly adjust their human-directed behavior to the actions of their partners, which indicate their willingness and abilities to help them when they are faced with a problem. Here, in two experiments, we confronted dogs—after initially allowing them to learn how to manipulate an apparatus—with two problem situations: with an empty apparatus and a blocked apparatus. In Experiment 1, we showed that dogs looked back at their owners more when the owners had previously encouraged them, independently from the problem they faced. In Experiment 2, we provided dogs with two experimenters and allowed them to learn through an initial phase that each of the experimenters could solve one of the two problems: the Filler re-baited the empty apparatus and the Helper unblocked the blocked apparatus. We found that dogs could learn to recognize the ability of the Filler and spent time close to her when the apparatus was empty. Independently from the problem, however, they always approached the Helper first. The results of the present study indicate that dogs may have a limited understanding of physical problems and how they can be solved by a human partner. Nevertheless, dogs are able to adjust their behavior to situation-specific characteristics of their human partner’s behavior.     

Twenty years of applied behavior analysis in treating the most severe problem behavior: Lessons learned

This paper reviews some of the author's experiences during the past 20 years of treating severe problem behavior. Factors that represent barriers to success are identified and discussed, as are factors that contribute to the development of successful treatment programs. Barriers to success include the inherent reactive nature of human services and educational systems, expertise problems, systems problems, information gaps, programming problems, characteristics of problem behavior, and maintenance problems. Some new programmatic directions are suggested for overcoming the various treatment barriers. The paper concludes with strategies and factors to consider that will ensure long-term success in the treatment of severe problem behavior.     

Optimal experimental design for a class of bandit problems

Bandit problems are a class of sequential decision-making problems that are useful for studying human decision-making, especially in the context of understanding how people balance exploration with exploitation. A major goal of measuring people’s behavior using bandit problems is to distinguish between competing models of their decision-making. This raises a question of experimental design: How should a set of bandit problems be designed to maximize the ability to discriminate between models? We apply a previously developed design optimization framework to the problem of finding good bandit problem experiments, and develop computational sampling schemes for implementing the approach. We demonstrate the approach in a number of simple cases, varying the priors on parameters for some standard models. We also demonstrate the approach using empirical priors, inferred by hierarchical Bayesian analysis from human data, and show that optimally designed bandit problems significantly enhance the ability to discriminate between competing models.     

Spatial and contextual factors in human performance on the travelling salesperson problem

The travelling salesperson problem (TSP) provides a realistic and practical example of a visuo-spatial problem-solving task. In previous research, we have found that the quality of solutions produced by human participants for small TSPs compares well with solutions from a range of computer algorithms. We have proposed that the ability of participants to find solutions reflects the natural properties of human perception, solutions being found through global perceptual processing of the problem array to extract a best figure from the TSP points. In this paper, we extend the study of human performance on the task in order to understand further how human abilities are utilised in solving real-world TSPs. The results of experiment 1 show that high levels of solution quality are maintained in solving larger TSPs than had been investigated previously with human participants, and that the presence of an implied real-world context in the problems has no effect upon performance. Experiment 2 demonstrated that the presence of regularity in the point layout of a TSP can facilitate performance. This was confirmed in experiment 3, where effects of the internality of point clusters were also found. All three experiments were consistent with a global, perceptually based approach to the problem by participants. We suggest that the role of perceptual processing in spatial problem-solving is an important area for further research in both theoretical and applied domains.     

An Approach for Determining the Mix between Human and Automated Service Providers

With the growing popularity of self‐service technologies (SST), businesses have to determine how to combine traditional human service with SST technologies. These mix problems have not received adequate attention in the research literature. This paper offers a decision model for solving mix problems between human service channels and SSTs. The decision problem cannot be determined as a traditional max or min problem and it is fuzzy. In fuzzy problems, the decision maker (DM) is considered as the focal point with the relevant knowledge and preferences and therefore, the objective is to maximize the DM's preferences. This paper develops a decision support system for eliciting a DM's value function when the service channels are considered to be substitutes, but when the rate of substitution may change and then uses a linear‐fractional model to fit the DM's data and optimize the DM's preferences. A service mix problem for a grocery store with human service providers and SSTs is used to demonstrate how the decision model can be employed to analyze and solve service mix decision making problems. Copyright © 2014 John Wiley & Sons, Ltd.     

An evaluation of human act generation performance

Act generation is important in decision making in ill-defined problems where the subject must synthesize actions that might solve the problem. Two experiments explored human abilities to generate actions which might solve ill-defined decision problems. Subjects were given unlimited time to suggest as many solutions as possible. Their suggestions were compared to a hierarchical structural model of the actions developed by the experimenters that could be taken to solve the problem. Although subjects were capable of generating several actions that might be worth taking, their suggestions were far from complete. The second experiment replicated and extended these results by introducing instructions to generate quality actions. It employed a structural model derived from cluster analysis, an improved utility estimation technique, and offered substantial monetary incentives for quality or quantity of actions generated. This study confirmed the general conclusion that subjects fail to generate important high-utility actions. The implications of this result are discussed in respect to decision analysis in which a complete structural model of the decision problem is highly desirable.     

国外关于问题解决的研究及其教学意义

提高学生解决问题能力已经成为现代教育的一个重要目标。问题解决就是由一定情境引起的,按照一定的目标,应用各种认知活动、技能等,经过一系列思维操作,使问题得以解决的过程。本文综述了问题解决的过程模型,影响问题解决的心理因素,并寻找其教学意义,以期给当前我国教育与教学改革提供参考。     

General intelligence as a domain-specific adaptation

General intelligence (g) poses a problem for evolutionary psychology's modular view of the human brain. The author advances a new evolutionary psychological theory of the evolution of general intelligence and argues that general intelligence evolved as a domain-specific adaptation for the originally limited sphere of evolutionary novelty in the ancestral environment. It has accidentally become universally important merely because we now live in an evolutionarily novel world. The available data seem to support the author's contention that intelligent people can solve problems better than less intelligent people only if the problems are evolutionarily novel, and they have no advantage in solving evolutionarily familiar problems. This perspective can also solve some empirical anomalies, such as the "central theoretical problem of human sociobiology" (D. R. Vining, 1986, p. 167) and the geographic distribution of general intelligence throughout the world.     

Selecting analogous problems: Similarity versus inclusiveness

Students were asked to select one of two analogous problems in order to solve algebra word problems. In Experiment 1, one problem was less inclusive and the other was more inclusive than a test problem. The students judged the complexity and similarity of problems, selected analogous problems, and used the solutions to solve test problems. They performed significantly better on the test problems when given the more inclusive solutions, but used perceived similarity rather than inclusiveness to select analogous problems. The same pattern of results occurred in Experiment 2, in which isomorphic problems replaced the more inclusive problems. The results show that students are deficient in selecting good analogies, both from the same category (Experiment 1) and from a different category (Experiment 2). Students who saw the analogous solutions (Experiment 3) or were majoring in mathematics (Experiment 4) were more likely to select an isomorphic problem over a less inclusive problem, but were not more likely to select a more inclusive over a less inclusive problem.     

Problem solving in semantically rich domains: An example from engineering thermodynamics

Recent research on human problem solving has largely focused on laboratory tasks that do not demand from the subject much prior, task-related information. This study seeks to extend the theory of human problem solving to semantically richer domains that are characteristic of professional problem solving. We discuss the behavior of a single subject solving problems in chemical engineering thermodynamics. We use as a protocol-encoding device a computer program called SAPA which also doubles as a theory of the subject's problem-solving behavior. The subject made extensive use of means-ends analysis, similar to that observed in semantically less rich domains, supplemented by recognition mechanisms for accessing information in semantic memory.     

Solving probabilistic and statistical problems: a matter of information structure and question form

Is the human mind inherently unable to reason probabilistically, or is it able to do so only when problems tap into a module for reasoning about natural frequencies? We suggest an alternative possibility: naive individuals are able to reason probabilistically when they can rely on a representation of subsets of chances or frequencies. We predicted that naive individuals solve conditional probability problems if they can infer conditional probabilities from the subset relations in their representation of the problems, and if the question put to them makes it easy to consider the appropriate subsets. The results of seven studies corroborated these predictions: when the form of the question and the structure of the problem were framed so as to activate intuitive principles based on subset relations, naive individuals solved problems, whether they were stated in terms of probabilities or frequencies. Otherwise, they failed with both sorts of information. The results contravene the frequentist hypothesis and the evolutionary account of probabilistic reasoning.     

Of mice and men: virtual Hebb-Williams mazes permit comparison of spatial learning across species

We developed a computer-generated virtual environment to test humans, for the first time, on the Hebb-Williams mazes. The goal was to provide a standardized test that could be used to directly compare human performance with that of C57BL/6J mice performing in real versions of the mazes. Such a comparison seems crucial if conclusions regarding genetic manipulations of rodents are to be mapped onto human cognitive disorders. The learning curves across species were strikingly similar, lending support to the rodent model of human spatial memory. Humans learned faster than rodents in both the acquisition and the test portions of the protocol, and females of both species were less efficient in solving these problems than males. These results represent the first modern comparison of human and rodent learning that uses the same test of spatial problem solving.     

The role of analogy in transfer between similar problem states

The study investigated the effect of transfer between two problems having similar (homomorphic) problem states. The results of three experiments revealed that although transfer occurred between repetition of the same problems, transfer occurred between the Jealous Husbands problem and the Missionary—Cannibal problem only when (a) Ss were told the relationship between the two problems and (b) the Jealous Husbands problem was given first. The results are related to the formal structure of the problem space and to alternative explanations of the use of analogy in problem solving. These include memory for individual moves, memory for general strategies, and practice in applying operators.     

Mathematical model building in the solution of mechanics problems: Human protocols and the MECHO trace

This paper describes model building and manipulation in the solution of problems in mechanics. An automatic problem solver, MECHO, solving problems in several areas of mechanics, employs (1) a knowledge base representing the semantic content of the particular problem area, (2) a means-ends search strategy similar to GPS to produce sets of simultaneous equations and (3) a “focusing” technique, based on the data within the knowledge base, to guide the GSP-like search through possible equation instantiations. Sets of predicate logic statements are employed to describe this model building activity. These clauses are used to give information content to the knowledge base and provide both basis and guidance for the goal driven search. It is hypothesized that humon subjects solving mechanics problems employ similar model building techniques. Protocols of several subjects are presented and comparisons are drawn with the traces of the automated problem solver. Adjustments are made to the program to provide a better fit of traces to protocols. Some implications are presented for using a rule based model for describing human problem solving performance in solving mechanics problems.     

中转站系统框架的心理学研究

通过总结人类问题求解的特点,我们提出了问题求解的一种中转站系统框架。本文采用口语报告分析所进行的研究表明该框架是一种问题求解方面的知识结构和有效的启发式求解策略。实验证明了次序性、熟悉性、类似性和系统控制性是决定求解能力和效率的关键因素。说明了中转站及其排序、强规则和弱规则及其相互配合、以及中转站系统的控制策略在较大程度上反映和模拟了人在求解复杂的专门问题时的知识结构和控制过程。     

Trials per problem and age as factors in learning set formation of children

Discrimination learning set performance was examined in preschool children as a function of age and number of trials per problem. Subjects 3, 4, 5, and 6 years old were trained over three 72-trial sessions. Half of the children at each age were given problems three trials in length, and half received 12-trial problems. As predicted, younger children (3 and 4-year-olds) performed significantly better for 12 than for three trials per problem, whereas no differences in performance were evident for older children. Response pattern analyses revealed that younger children, especially those receiving less exposure within problems, produced a higher proportion of stimulus alternation and position determined response sequences than did older children. These results contradict the commonly held assumption that learning set acquisition is simply a function of the total number of trials presented and indicate that the amount of exposure to individual problems is a factor in problem solution for young human subjects.