Two generations of psychologists have been interested in understanding binary choice under uncertainty. In the 1970s and 1980s, researchers assumed that people rely on a two-stage magnitude comparison process to make these decisions (Banks, 1977; Moyer & Dumais, 1978). More recently, the focus has shifted to approaches that rely on probabilistic cues and simple heuristics (Gigerenzer & Goldstei...

Web extraction is the task of turning unstructured HTML into structured data. Previous approaches rely exclusively on detecting repeated structures in result pages. These approaches trade intensive user interaction for precision. In this paper, we introduce the Amber (“Adaptable Model-based Extraction of Result Pages”) system that replaces the human interaction with a domain ontology applicable...

A key challenge in the design of Virtual Musical instruments (VMIs) is finding expressive, playable, learnable mappings from gesture to sound that progressively reward practice by performers. Designing such mappings can be particularly demanding in the case of non-contact musical instruments, where physical cues can be scarce. Unaided intuition works well for many instrument designers, but othe...

Memory-based heuristics are a popular and effective class of admissible heuristic functions. However, corruptions to memory they use may cause these heuristics to become inadmissible. Corruption can be caused by the physical environment due to radiation and network errors, or it can be introduced voluntarily in order to decrease energy consumption. We introduce memory error correction schemes t...

In this paper we present a system for producing good examination timetables, by integrating the search capabilities of computer heuristics and the cognitive capabilities of timetabling users. We allow users to view and easily edit a cognitively manageable representation of each timetable, using the STARK (Semantically Transparent Approach to Representing Knowledge) approach. Further, we allow u...

We examine search algorithms for games with imperfect information. We rst investigate Monte Carlo sampling, showing that for very simple game trees the chance of nding an optimal strategy rapidly approaches zero as size of the tree increases. We identify the reasons for this sub-optimality, and show that the same problems occur in Bridge, a popular real-world imperfect information game. We then...

The passive autofocus mechanism is an essential feature of modern digital cameras and needs to be highly accurate to obtain quality pictures. In this paper, we address the problem of finding a lens position where the image is in focus. We show that supervised machine learning techniques can be used to construct heuristics for a hill-climbing approach to finding such positions which out-performs...

The quantitative analysis of software projects can provide insights that let us better understand open source and other software development projects. An important variable used in the analysis of software projects is the amount of work being contributed, the commit size. Unfortunately, post-facto, the commit size can only be estimated, not measured. This paper presents several algorithms for e...

A lot of heuristic approaches have been explored in the last two decades in order to tackle large size optimization problems. These areas include parallel meta-heuristics, hybrid meta-heuristic, and cooperative search algorithms. Different taxonomies have been proposed in the literature for parallel and hybrid meta-heuristics. In these taxonomies, one can realize that cooperative search algorit...

In this paper we propose a new tabu search hyperheuristic which makes individual low level heuristics tabu dynamically using an analogy with the Binary Exponential Back Off (BEBO) method used in network communication. We compare this method to a reduced Variable Neighbourhood Search (rVNS), greedy and random hyperheuristic approaches and other tabu search based heuristics for a complex real wor...