Heuristics Made Easy: An Effort-Reduction Framework

Behavioral Science, 1980

This article examines decision making in living systems at the level of the organism.

International Journal of Interactive Multimedia and Artificial Intelligence

New heuristics are often further developments of established heuristics and emerge, for example, by making an adjustment to another context being examined, such as mobile end devices [7], persuasive technologies [8] or patient safety for medical devices [9].

Computational Intelligence, 1985

From the mid-1950's to the present the notion of a heuristic has played a crucial role in the A1 researchers' descriptions of thcir work. What has not been generally noticed is that different researchers have often applied the term to rather different aspects of their programs. Things that would be called a heuristic by one researcher would not be so called by others. This is because many heuristics embody a variety of different features, and the various researchers have emphasized different ones of these features as being essential to being a heuristic. This paper steps back from any particular research program and investigates the question of what things, historically, have been thought to be central to the notion of a heuristic and which ones conflict with others. After analyzing the previous definitions and examining current usage of the term, a synthesizing definition is provided. The hope is that with this broader account of 'heuristic' in hand, researchers can benefit more fully from the insights of others, even if those insights are couched in a somewhat alien vocabulary.

In this paper, human heuristics have been identified that provide close to optimal solutions when solving Capacitated Vehicle Routing Problems (CVRPs). Results from previous experiments showed humans can produce good solutions relatively fast that compete with computer-based methods giving further support to previous research on Traveling Salesman Problems (TSPs). Multiple Regression analyses have been conducted to show the best heuristics adopted by participants and that lead to better CVRP solutions. Identified heuristics are categorized in visuospatial and arithmetic heuristics. Visuospatial heuristics (e.g. Clustering, Anchoring) performed better than the arithmetic (e.g. Balancing). Strategy switching appears to be a critical step within CVRP solutions suggesting that heuristics adopted are fast yet notso-frugal, complimenting the fast and frugal toolkit. Results are discussed under the light of problem-solving theories and in terms of how best human heuristics can inform the current state-of-art computational algorithms used in optimization problem solving.

Cognitive Processing, 2009

In their comment on Marewski et al. (good judgments do not require complex cognition, 2009) Evans and Over (heuristic thinking and human intelligence: a commentary on Marewski, Gaissmaier and Gigerenzer, 2009) conjectured that heuristics can often lead to biases and are not error free. This is a most surprising critique. The computational models of heuristics we have tested allow for quantitative predictions of how many errors a given heuristic will make, and we and others have measured the amount of error by analysis, computer simulation, and experiment. This is clear progress over simply giving heuristics labels, such as availability, that do not allow for quantitative comparisons of errors. Evans and Over argue that the reason people rely on heuristics is the accuracyeffort trade-off. However, the comparison between heuristics and more effortful strategies, such as multiple regression, has shown that there are many situations in which a heuristic is more accurate with less effort. Finally, we do not see how the fast and frugal heuristics program could benefit from a dual-process framework unless the dual-process framework is made more precise. Instead, the dual-process framework could benefit if its two ''black boxes'' (Type 1 and Type 2 processes) were substituted by computational models of both heuristics and other processes.

2014

The obvious way to use several admissible heuristics in A ∗ is to take their maximum. In this paper we aim to re-duce the time spent on computing heuristics. We discuss Lazy A∗, a variant of A ∗ where heuristics are evaluated lazily: only when they are essential to a decision to be made in the A ∗ search process. We present a new ra-tional meta-reasoning based scheme, rational lazy A∗, which decides whether to compute the more expensive heuristics at all, based on a myopic value of information estimate. Both methods are examined theoretically. Em-pirical evaluation on several domains supports the theo-retical results, and shows that lazy A ∗ and rational lazy A ∗ are state-of-the-art heuristic combination methods. 1

Humanities and Social Sciences Communications

Heuristics are often characterized as rules of thumb that can be used to speed up the process of decision-making. They have been examined across a wide range of fields, including economics, psychology, and computer science. However, scholars still struggle to find substantial common ground. This study provides a historical review of heuristics as a research topic before and after the emergence of the subjective expected utility (SEU) theory, emphasising the evolutionary perspective that considers heuristics as resulting from the development of the brain. We find it useful to distinguish between deliberate and automatic uses of heuristics, but point out that they can be used consciously and subconsciously. While we can trace the idea of heuristics through many centuries and fields of application, we focus on the evolution of the modern notion of heuristics through three waves of research, starting with Herbert Simon in the 1950s, who introduced the notion of bounded rationality and s...

Information Sciences, 1979

The purpose of this paper is to propose a formal framework for structuring and embedding the heuristic information, in o_rder to allow an algorithmic computation, in quite general cases, of the evaluation function An) of the classical Hart-Nilsson-Raphael algorithm. The notion of semantic graph is first introduced, in which the atomic notion of node is expanded by associating to it an internal structure where the heuristic information is inserted. It is proved that h(n) can be computed by solving an auxiliaq problem, obtained from the original one by adding new arcs, and of s4er complexity than that onw. A new algorithm is then defined for the computation of h(n) and for the determination of minimal solutions. The validity of the model proposed is discussed in detail.

The Quarterly Journal of Experimental Psychology, 2006

Human performance on instances of computationally intractable optimization problems, such as the travelling salesperson problem (TSP), can be excellent. We have proposed a boundary-following heuristic to account for this finding. We report three experiments with TSPs where the capacity to employ this heuristic was varied. In Experiment 1, participants free to use the heuristic produced solutions significantly closer to optimal than did those prevented from doing so. Experiments 2 and 3 together replicated this finding in larger problems and demonstrated that a potential confound had no effect. In all three experiments, performance was closely matched by a boundary-following model. The results implicate global rather than purely local processes. Humans may have access to simple, perceptually based, heuristics that are suited to some combinatorial optimization tasks. How humans solve complex problems or make decisions in the face of uncertain information has long held the attention of psychologists. Debate has typically focused upon divisions between proponents of normative systems such as Bayesian inference and formal logic as the basis of human thought (e.g., Braine & O'Brien, 1991; Oaksford & Chater, 1994; Rips, 1994) and those who describe human thinking in terms of the operation of heuristics that guide the discovery of plausible solutions. The latter approach can be further divided between proponents of heuristics determined by properties of the space of possible solutions, such as means-ends analysis (e.g., Newell & Simon, 1972), representativeness, and availability (Tversky & Kahneman, 1973) and structural analogy (Anderson, 1993), and proponents of heuristics determined by the ecological

2013

— In this paper we proposed an enhanced classification of heuristics and developed a unified heuristic classifying scheme and templates for future researchers to conduct a fair heuristic performance. The process involved a thorough diagnostic of the proposed heuristic classification schemes in order to find unique characteristics that can assist us to distinguish each heuristic. We started by carrying out a survey of the heuristic classification schemes and templates previously proposed in the literature. We discovered that most of heuristic classifying schemes and templates proposed in the literature looked at individual heuristic class without integrating together all the classes of heuristics, thus justifying the need for a unified heuristic classifying scheme and templates. We also discovered some common interlinked and interrelated templates of classifying heuristics within which sub-templates were identified. The major challenge in classifying heuristics is basically on discovering unique features (of these heuristics and their variants) that are capable to unlock the element of heuristics belonging to the classifying templates.

Procedia Engineering, 2015

Heuristics are widely accepted and used as tools for inventive problem solving. A problem when using heuristics is that their number is relatively high (469 heuristics were found in this research) and keeps increasing. This amount of heuristics makes it necessary for problem solvers to spend a significant amount of time in understanding them, finding the most applicable ones to their specific situation and using them. This article presents a first step towards decreasing this complexity. We synthesized the available inventive problem solving heuristics in a single list. The development of this list involved the identification of the inventive problem solving heuristics in literature, followed by analysis and comparison, resulting in a final list of 263 heuristics. The authors hope that this list can save problem solvers' time and become a basis for a future, essential set of inventive problem solving heuristics.

Handbook of …, 2010

The current state of the art in hyper-heuristic research comprises a set of approaches that share the common goal of automating the design and adaptation of heuristic methods to solve hard computational search problems. The main goal is to produce more generally applicable search methodologies. In this chapter we present an overview of previous categorisations of hyper-heuristics and provide a unified classification and definition, which capture the work that is being undertaken in this field. We distinguish between two main hyper-heuristic categories: heuristic selection and heuristic generation. Some representative examples of each category are discussed in detail. Our goals are to clarify the main features of existing techniques and to suggest new directions for hyper-heuristic research.

Computers & Chemical Engineering, 2004

The efforts to develop solution algorithms for combinatorial optimization problems can be classified broadly into two categories, heuristic and rigorous approaches. These two approaches have ever-conflicting advantages and disadvantages in their computational load and solution quality. Due to the computational infeasibility of the rigorous approach for many practical combinatorial applications problem-specific heuristics are prevalent, even though they cannot provide any guarantee on the solution's quality. In this paper, we propose a novel way to improve upon a solution obtained from heuristics by applying dynamic programming to the subset of the states visited during simulation of the heuristics. The method represents a way to take a family of solutions and patch them together as an improved solution. However, 'patching' is accomplished in state space rather than in solution space. We develop and apply the approach to a new traveling salesman problem (TSP) variant, which illustrates the important notions of optional and conditional tasks in planning and scheduling applications, to examine the degree of improvement that can be obtained by the method. For a small problem, we compare the quality of the solution with the globally optimal one. The proposed method can be generalized to other planning and scheduling problems as long as a reasonable set of heuristics exist for their solution.

Proceedings of the 13th annual conference companion on Genetic and evolutionary computation - GECCO '11, 2011