This work explores the benefits of using user models for plan recognition problems in a real-world application. Selfinterested agents are designed for the prediction of resource usage in the UNIX domain using a stochastic approach to automatically acquire regularities of user behavior. Both sequential information from the command sequence and relational information such as system’s responses an...

In this report, first we give a survey of the work in plan recognition field, including the evolution of different approaches, their strength and weaknesses. Then we propose two decision-theoretic approaches to plan recognition problem, which explicitly take outcome utilities into consideration. One is an extension within the probabilistic reasoning framework, by adding utility nodes to belief ...

This paper explores the bene ts of adapting techniques from inductive concept learning to plan recognition. A powerful notion in concept learning is characterizing inductive systems by their bias, i.e. the implicit assumptions which justify the conclusions an inductive system produces. We present a spectrum of possible biases for plan recognition. We evaluate these biases based on how accuratel...

Plan recognition does not work the same way in stories and in "real life, (people tend to jump to conclusions more in sto­ ries). We present a theory of this, for the particular case of how objects in stories (or in life) influence plan recognition decisions. We provide a Bayesian network formaliza­ tion of a simple first-order theory of plans, and show how a particular network param­ eter seem...

world states is made feasible by the structural property of the abstraction mechanism. That is, the abstract states essentially preserve the structure of the concrete world states at the level of type-generalized predicates. The example in Figure 6 shows 7 State literal from blocksworld planning domain (on blockA blockB) would be type-generalized to (on OBJECT OBJECT), as type of instances bloc...

SET-PR is a novel case-based recognizer that is robust to three kinds of input errors arising from imperfect observability, namely missing, mislabeled and extraneous actions. We extend our previous work on SET-PR by empirically studying its efficacy on three plan recognition datasets. We found that in the presence of higher input error rates, SET-PR significantly outperforms alternative approac...

There are many commercial tools that address various aspects of the Year 2000 problem. None of these tools, however, make any documented use of plan-based techniques for automated concept recovery. This implies a general perception that plan-based techniques is not useful for this problem. This paper argues that this perception is incorrect and these techniques are in fact mature enough to make...

Interactive narratives suffer from the narrative paradox: the tension that exists between providing a coherent narrative experience and allowing a player free reign over what she can manipulate in the environment. Knowing what actions a player in such an environment intends to carry out would help in managing the narrative paradox, since it would allow us to anticipate potential threats to the ...

The recognition of information in floor plan data requires the use detection and segmentation models. However, relying on several single-task models can result ineffective utilization relevant when there are multiple tasks present simultaneously. To address this challenge, we introduce MuraNet, an attention-based multi-task model for data. In adopt a unified encoder called MURA as backbone with...