Modern multicore computers provide an opportunity to parallelize plan recognition algorithms to decrease runtime. Viewing plan recognition as parsing based on a complete breadth first search, makes ELEXIR (engine for lexicalized intent recognition) (Geib 2009; Geib and Goldman 2011) particularly suited for parallelization. This article documents the extension of ELEXIR to utilize such modern computing platforms. We will discuss multiple possible algorithms for distributing work between parallel threads and the associated performance wins.
My Ph.D. dissertation (Calistri 1990) extends traditional methods of plan recognition to handle situations in which agents have flawed plans. This extension involves solving two problems: determining what sorts of mistakes people make when they reason about plans and figuring out how to recognize these mistakes when they occur. I have developed a complete classification of plan-based misconceptions, which categorizes all ways that a plan can fail, and I have developed a probabilistic interpretation of these misconceptions that can be used in principle to guide a best-first search algorithm. I have also developed a program called Pathfinder that embodies a practical implementation of this theory.
"MANY EFFORTS have been made to discriminate, categorize, and quantitate patterns, and to reduce them into a usable machine language. The results have ordinarily been methods or devices with a high degree of specificity. For example, some devices require a special type font; others can read only one type font; still others require magnetic ink. We have an interest in decision-making circuits with the following qualities: (1) measurable high reliability in decision making, (2) either a high or a low reliability input, and (3) possibly low reliability components. The high specificity of the devices and methods mentioned above was felt to be a drawback for our purposes. All of these approaches prove upon inspection to center upon analysis of the specific characteristics of patterns into parts, followed by a synthesis of the whole from the parts. In these studies, pattern recognition of the whole, that is, Gestalt recognition, was chosen as a more fruitful avenue of approach and as a satisfactory problem for the initial phases of the over-all study."Proceedings of the Eastern Joint Computer Conference, pp. 225-232, New York: Association for Computing Machinery