Many real-world optimization problems involve multiple objectives which are often conflicting. When conventional heuristic search algorithms such as A* and IDA* are used for solving such problems, then these problems have to be modeled as simple cost minimization or maximization problems. The task of modeling such problems using a single valued criterion has often proved difficult [6]. The prob...

This paper has three main contributions to our understanding of fixed-depth minimax search: (A) A new formulation for Stockman’s SSS* algorithm, based on Alpha-Beta, is presented. It solves all the perceived drawbacks of SSS*, finally transforming it into a practical algorithm. In effect, we show that SSS* = α-β + transposition tables. The crucial step is the realization that transposition tabl...

This article addresses the multiplication of one data sample with multiple constants using addition/subtraction and shift operations, i.e., the multiple constant multiplications (MCM) operation. In the last two decades, many efficient algorithms have been proposed to implement the MCM operation using the fewest number of addition and subtraction operations. However, due to the NP-hardness of th...

A new model for the execution of Prolog programs, called MEM (Multipath Execution Model), which combines a depth-first and breadth-first exploration of the search tree is presented. The breadth-first search allows more than one path (multiple potential solutions) to be explored at the same time. In this way, the computational cost of traversing the whole search tree associated to a program can ...

Although Breadth-First Search (BFS) has several advantages over Depth-First Search (DFS) its prohibitive space requirements have meant that algorithm designers often pass it over in favor of DFS. To address this shortcoming, we introduce a theory of Efficient BFS (EBFS) along with a simple recursive program schema for carrying out the search. The theory is based on dominance relations, a long s...

There are two major paradigms for linear-space heuristic search: iterative deepening (IDA*) and recursive best-first search (RBFS). While the node regeneration overhead of IDA* is easily characterized in terms of the heuristic branching factor, the overhead of RBFS depends on how widely the promising nodes are separated in the search tree, and is harder to anticipate. In this paper, we present ...

Marginal MAP is known to be a difficult task for graphical models, particularly because the evaluation of each MAP assignment involves a conditional likelihood computation. In order to minimize the number of likelihood evaluations, we focus in this paper on best-first search strategies for exploring the space of partial MAP assignments. We analyze the potential relative benefits of several best...

Breadth-first search (BFS) and depth-first search (DFS) are the two most fundamental search algorithms. We derive approximations of their expected runtimes in complete trees, as a function of tree depth and probabilistic goal distribution. We also demonstrate that the analytical approximations are close to the empirical averages for most parameter settings, and that the results can be used to p...

Prior research has shown that the effect of information systems (IS) domain knowledge and application domain knowledge on problem solving is contingent on task type (Khatri et al. 2006). We build on this study by engaging in an in-depth analysis of how both these types of knowledge influence one type of task referred to as “schema-based problem solving” task. Our theoretical analysis is based o...