In a recent paper, we have shown that Plan Recognition over STRIPS can be formulated and solved using Classical Planning heuristics and algorithms (Ramirez and Geffner 2009). In this work, we show that this formulation subsumes the standard formulation of Plan Recognition over libraries through a compilation of libraries into STRIPS theories. The libraries correspond to AND/OR graphs that may b...

Based on the assumption that a rational agent will adopt a plan that maximizes the expected utility, we present a utility-based approach to plan recognition problem in this paper. The approach explicitly takes the observed agent’s preferences into consideration, and computes the estimated expected utilities of plans to disambiguate competing hypotheses. Online plan recognition is realized by in...

This paper documents extending the ELEXIR (Engine for LEXicalized Intent Recognition) system (Geib 2009; Geib and Goldman 2011) with a world model. This is a significant increase in the expressiveness of the plan recognition system and allows a number of additions to the algorithm, most significantly conditioning probabilities for recognized plans on the state of the world during execution. Sin...

We present a technique to further narrow the gap between recipe-based and domain theory-based plan recognition through decompositional planning, a planning model that combines hierarchical reasoning as used in hierarchical task networks, and least-commitment refinement reasoning as used in partial-order causal link planning. We represent recipes through decompositional planning operators and us...

Di erent program understanding algorithms often use di erent representational frameworks and take advantage of numerous heuristic tricks. This situation makes it is di cult to compare these approaches and their performance. This paper addresses this problem by proposing constraint satisfaction as a general framework for describing program understanding algorithms, demonstrating how to tranform ...

Based on the assumption that a rational agent will adopt a plan that maximizes the expected utility, we present a utility-based approach to plan recognition problem in this paper. The approach explicitly takes the observed agent’s preferences into consideration, and computes the estimated expected utilities of plans to disambiguate competing hypotheses. Online plan recognition is realized by in...

Partial observability of the domain prevents the application of many plan recognition algorithms to real world problems. This paper presents a treatment for partial observability for a specific probabilistic plan recognition algorithm and an initial analysis of the runtime impact.

This paper identifies some of the assumptions that prevent the effective application of existing plan recognition technology to intrusion detection in computer systems. It then presents a new algorithm for plan recognition that does not have these limitations.

Multi-Agent Plan Recognition (MAPR) seeks to identify the dynamic team structures and team behaviors from the observations of the activity-sequences of a set of intelligent agents, based on a library of known team-activities (plan library). It has important applications in analyzing data from automated monitoring, surveillance, and intelligence analysis in general. In this paper, we formalize M...

Generative Process Planning systems provide a powerful means of generating manufacturing process plans from a product model. However, the vast majority of current commercial and experimental systems are limited in several ways: they tend to be specialized for metal-machining processes, they tend to focus on one state of the product at a time, and in general they lack an intelligent product mode...