In incomplete single-agent search, it is generally accepted that deeper searches produces better results. It has recently been discovered, though, that this is not always the case – such behavior has been termed pathological. This paper identifies two properties of search trees that cause pathological behavior and explains how they produce the pathology. A number of different heuristic function...

We study the behavior of ASAT, a heuristic for solving satisfiability problems by stochastic local search near the SAT/UNSAT transition. The heuristic is focused, i.e. only variables in unsatisfied clauses are updated in each step, and is significantly simpler, while similar to, walksat or Focused Metropolis Search. We show that ASAT solves instances as large as N = 10 in linear time, on averag...

Malware is a software which is designed with an intent to damage a network or computer resources. Today, the emergence of malware is on boom letting the researchers develop novel techniques to protect computers and networks. The three major techniques used for malware detection are heuristic, signature-based, and behavior based. Among these, the most prevalent is the heuristic based malware det...

A promising approach to solving large state-space search problems is to integrate heuristic search with symbolic search. Recent work shows that a symbolic A* search algorithm that uses binary decision diagrams to compactly represent sets of states outperforms traditional A* in many domains. Since the memory requirements of A* limit its scalability, we show how to integrate symbolic search with ...

We show how dynamic (changing) environments can affect choice behavior, and highlight the challenges that recent models face in explaining the learning and selection of heuristic strategies under such conditions, especially when decisions are made using only a small subset of the available information. We propose an enhanced modeling framework that includes a trial-by-trial implementation of a ...

This paper introduces a novel approach to performance refinement of a heuristic algorithm for combinatorial optimization. The proposed Adaptive Tabu Search (A–TS) algorithm introduces adaptive behavior in the traditional Tabu Search algorithm. The adaptive nature of this algorithm is based on two adaptive coefficients that drive the heuristic. Choosing appropriate values for these parameters ha...

In this paper we present a new bidirectional heuristic search algorithm. Our algorithm can be viewed as a perimeter search algorithm, and it uses a new technique for reducing the number of heuristic evaluations. We also prove some general results on the behavior of iterative deepening perimeter search algorithms, and we discuss some new “lazy evaluation” techniques for improving their performan...

In this paper we describe an analysis, using evolutionary game theory, of two double auction markets—the clearing house auction and the continuous double auction. We use heuristic-strategy approximation to analyse two broad classes of traders. One heuristic strategy approximates human behavior, and the other is a simple automated strategy, so our analysis permits us to predict the evolution of ...

The problem of mapping tasks and communications onto multiple machines and networks in a heterogeneous computing environment has been shown to be NP complete. Therefore, the development of heuristic techniques to find nearoptimal solutions is required. Many different types of mapping heuristics have been developed in recent years. However, selecting the best heuristic to use in any given scenar...

In this paper we investigate human exploration/exploitation behavior in sequential-decision making tasks. Previous studies have suggested that people are suboptimal at scheduling exploration, and heuristic decision strategies are better predictors of human choices than the optimal model. By incorporating more realistic assumptions about subject’s knowledge and limitations into models of belief ...