46 research outputs found
Policy Recognition in the Abstract Hidden Markov Model
In this paper, we present a method for recognising an agent's behaviour in
dynamic, noisy, uncertain domains, and across multiple levels of abstraction.
We term this problem on-line plan recognition under uncertainty and view it
generally as probabilistic inference on the stochastic process representing the
execution of the agent's plan. Our contributions in this paper are twofold. In
terms of probabilistic inference, we introduce the Abstract Hidden Markov Model
(AHMM), a novel type of stochastic processes, provide its dynamic Bayesian
network (DBN) structure and analyse the properties of this network. We then
describe an application of the Rao-Blackwellised Particle Filter to the AHMM
which allows us to construct an efficient, hybrid inference method for this
model. In terms of plan recognition, we propose a novel plan recognition
framework based on the AHMM as the plan execution model. The Rao-Blackwellised
hybrid inference for AHMM can take advantage of the independence properties
inherent in a model of plan execution, leading to an algorithm for online
probabilistic plan recognition that scales well with the number of levels in
the plan hierarchy. This illustrates that while stochastic models for plan
execution can be complex, they exhibit special structures which, if exploited,
can lead to efficient plan recognition algorithms. We demonstrate the
usefulness of the AHMM framework via a behaviour recognition system in a
complex spatial environment using distributed video surveillance data
Modeling Human Understanding of Complex Intentional Action with a Bayesian Nonparametric Subgoal Model
Most human behaviors consist of multiple parts, steps, or subtasks. These
structures guide our action planning and execution, but when we observe others,
the latent structure of their actions is typically unobservable, and must be
inferred in order to learn new skills by demonstration, or to assist others in
completing their tasks. For example, an assistant who has learned the subgoal
structure of a colleague's task can more rapidly recognize and support their
actions as they unfold. Here we model how humans infer subgoals from
observations of complex action sequences using a nonparametric Bayesian model,
which assumes that observed actions are generated by approximately rational
planning over unknown subgoal sequences. We test this model with a behavioral
experiment in which humans observed different series of goal-directed actions,
and inferred both the number and composition of the subgoal sequences
associated with each goal. The Bayesian model predicts human subgoal inferences
with high accuracy, and significantly better than several alternative models
and straightforward heuristics. Motivated by this result, we simulate how
learning and inference of subgoals can improve performance in an artificial
user assistance task. The Bayesian model learns the correct subgoals from fewer
observations, and better assists users by more rapidly and accurately inferring
the goal of their actions than alternative approaches.Comment: Accepted at AAAI 1
Hierarchical monitoring of people\u27s behaviors in complex environments using multiple cameras
We present a distributed, surveillance system that works in large and complex indoor environments. To track and recognize behaviors of people, we propose the use of the Abstract Hidden Markov Model (AHMM), which can be considered as an extension of the Hidden Markov Model (HMM), where the single Markov chain in the HMM is replaced by a hierarchy of Markov policies. In this policy hierarchy, each behavior can be represented as a policy at the corresponding level of abstraction. The noisy observations are handled in the same way as an HMM and an efficient Rao-Blackwellised particle filter method is used to compute the probabilities of the current policy at different levels of the hierarchy The novelty of the paper lies in the implementation of a scalable framework in the context of both the scale of behaviors and the size of the environment, making it ideal for distributed surveillance. The results of the system demonstrate the ability to answer queries about people\u27s behaviors at different levels of details using multiple cameras in a large and complex indoor environment.<br /
Factored state-abstract hidden Markov models for activity recognition using pervasive multi-modal sensors
Current probabilistic models for activity recognition do not incorporate much sensory input data due to the problem of state space explosion. In this paper, we propose a model for activity recognition, called the Factored State-Abtract Hidden Markov Model (FS-AHMM) to allow us to integrate many sensors for improving recognition performance. The proposed FS-AHMM is an extension of the Abstract Hidden Markov Model which applies the concept of factored state representations to compactly represent the state transitions. The parameters of the FS-AHMM are estimated using the EM algorithm from the data acquired through multiple multi-modal sensors and cameras. The model is evaluated and compared with other existing models on real-world data. The results show that the proposed model outperforms other models and that the integrated sensor information helps in recognizing activity more accurately