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Plan recognition is the general problem of inferring an agent's
plan(s) and goal(s) by observing its actions. Goal recognition
is a special type of plan recognition in which we only attempt to recognize
the agent's goal(s).
Plan and goal recognition have been used in a number of applications including user modeling, system intrusion detection, intelligent software help systems, and, the area we are most interested in, natural language understanding.
The goal of our work is to produce a goal recognizer which
Our Basic ApproachWe use machine learning techniques to predict the most likely goal after each observation. A goal consists of its schema and parameter values, all of which need to be predicted to make a full prediction. We separate the processes of goal schema and parameter recognition, and then find the most probable combination. The recognizer can make partial predictions by not predicting some or all of the parameter values (when it is not sure enough to predict them).
Plan CorporaWe estimate goal schema and parameter value probabilities using a plan corpus - a labeled corpus of goals and then actions an agent executed to achieve them. Obtaining a labeled corpus is difficult in the plan recognition domain, and very few exist.
We describe here two corpora we have gathered.
Human Corpus: The Linux Plan Corpus
Following the Unix Corpus, gathered by Neal Lesh at the University of Washington, we gathered the Linux Plan Corpus. Linux users at the University of Rochester were given a Linux goal (such as "find a file that ends in .tex") and then their Linux commands were recorded as they attempted to accomplish it.
The corpus consists of 457 successful plan sessions with 19 goal schemas. On average, a session consisted of 6.2 actions.
Artificial Corpus: The Monroe Plan Corpus
In most domains, obtaining a labeled corpus from humans is infeasible or impractical. In fact, even an unlabeled corpus would be difficult to get in many domains, since observing actions would require e.g., an advanced computer vision system to recognize actions. We propose a method of artificially creating corpora using an AI planner. Given a plan library for a domain, we stochastically choose a goal and generate a start state. Using a modified version of the SHOP2 Planner, we randomly generate a valid plan for the goal and start state. This process can be iterated to generate a corpus of any desired size. Using this process, we have created an artificial corpus for the Monroe domain - a disaster management domain. It contains 5000 plan sessions and 10 goal schemas with an average of 9.6 actions per session.
Current projectsThese are the extensions we are currently working on: