A heuristic model for the simulation of the deformation of elastic and spongy material for virtual reality applications

This paper presents a practical algorithm for the simulation of interactive deformation in a 3D polygonal mesh model. The algorithm combines the conventional simulation of deformation using a spring-mass-damping model, solved by explicit numerical integration, with a set of heuristics to describe certain features of the transient behaviour, to increase the speed and stability of solution. In particular, this algorithm was designed to be used in the simulation of synthetic environments where it is necessary to model realistically, in real time, the effect on non-rigid surfaces being touched, pushed, pulled or squashed. Such objects can be solid or hollow, and have plastic, elastic or fabric-like properties. The algorithm is presented in an integrated form including collision detection and adaptive refinement so that it may be used in a self-contained way as part of a simulation loop to include human interface devices that capture data and render a realistic stereoscopic image in real time. The algorithm is designed to be used with polygonal mesh models representing complex topology, such as the human anatomy in a virtual-surgery training simulator. The paper evaluates the model behaviour qualitatively and then concludes with some examples of the use of the algorithm.

How smart do you need to be to get it wrong? The role of cognitive capacity in the development of heuristic-based judgment

We examined the relationship between cognitive capacity and heuristic responding on four types of reasoning and decision-making tasks. A total of 84 children, between 5 years 2 months and 11 years 7 months of age, participated in the study. There was a marked increase in heuristic responding with age that was related to increases in cognitive capacity. These findings are inconsistent with the predominant dual-process accounts of reasoning and decision making as applied to development. We offer an alternative explanation of the findings, considering them in the context of recent claims concerning the role of working memory in contextualized reasoning.

Adaptive linear combination of heuristic orderings in constructing examination timetables

In this paper, we investigate adaptive linear combinations of graph coloring heuristics with a heuristic modifier to address the examination timetabling problem. We invoke a normalisation strategy for each parameter in order to generalise the specific problem data. Two graph coloring heuristics were used in this study (largest degree and saturation degree). A score for the difficulty of assigning each examination was obtained from an adaptive linear combination of these two heuristics and examinations in the list were ordered based on this value. The examinations with the score value representing the higher difficulty were chosen for scheduling based on two strategies. We tested for single and multiple heuristics with and without a heuristic modifier with different combinations of weight values for each parameter on the Toronto and ITC2007 benchmark data sets. We observed that the combination of multiple heuristics with a heuristic modifier offers an effective way to obtain good solution quality. Experimental results demonstrate that our approach delivers promising results. We conclude that this adaptive linear combination of heuristics is a highly effective method and simple to implement.

Exact and heuristic methods for solving Boolean games

Boolean games are a framework for reasoning about the rational behavior of agents whose goals are formalized using propositional formulas. Compared to normal form games, a well-studied and related game framework, Boolean games allow for an intuitive and more compact representation of the agents’ goals. So far, Boolean games have been mainly studied in the literature from the Knowledge Representation perspective, and less attention has been paid on the algorithmic issues underlying the computation of solution concepts. Although some suggestions for solving specific classes of Boolean games have been made in the literature, there is currently no work available on the practical performance. In this paper, we propose the first technique to solve general Boolean games that does not require an exponential translation to normal-form games. Our method is based on disjunctive answer set programming and computes solutions (equilibria) of arbitrary Boolean games. It can be applied to a wide variety of solution concepts, and can naturally deal with extensions of Boolean games such as constraints and costs. We present detailed experimental results in which we compare the proposed method against a number of existing methods for solving specific classes of Boolean games, as well as adaptations of methods that were initially designed for normal-form games. We found that the heuristic methods that do not require all payoff matrix entries performed well for smaller Boolean games, while our ASP based technique is faster when the problem instances have a higher number of agents or action variables.