Situation-Specific Intention Recognition for Human-Robot-Cooperation

Recognizing human intentions is part of the decision process in many technical devices. In order to achieve natural interaction, the required estimation quality and the used computation time need to be balanced. This becomes challenging, if the number of sensors is high and measurement systems are complex. In this paper, a model predictive approach to this problem based on online switching of small, situation-specific Dynamic Bayesian Networks is proposed. The contributions are an efficient modeling and inference of situations and a greedy model predictive switching algorithm maximizing the mutual information of predicted situations. The achievable accuracy and computational savings are demonstrated for a household scenario by using an extended range telepresence system

A Comparative Study of the Effect of Sensor Noise on Activity Recognition Models

To provide a better understanding of the relative strengths of Machine Learning based Activity Recognition methods, in this paper we present a comparative analysis of the robustness of three popular methods with respect to sensor noise. Specifically we evaluate the robustness of Naive Bayes classifier, Support Vector Machine, and Random Forest based activity recognition models in three cases which span sensor errors from dead to poorly calibrated sensors. Test data is partially synthesized from a recently annotated activity recognition corpus which includes both interleaved activities and a range of both temporally long and short activities. Results demonstrate that the relative performance of Support Vector Machine classifiers over Naive Bayes classifiers reduces in noisy sensor conditions, but that overall the Random Forest classifier provides best activity recognition accuracy across all noise conditions synthesized in the corpus. Moreover, we find that activity recognition is equally robust across classification techniques with the relative performance of all models holding up under almost all sensor noise conditions considered

Accuracy and Timeliness in ML Based Activity Recognition

While recent Machine Learning (ML) based techniques for activity recognition show great promise, there remain a number of questions with respect to the relative merits of these techniques. To provide a better understanding of the relative strengths of contemporary Activity Recognition methods, in this paper we present a comparative analysis of Hidden Markov Model, Bayesian, and Support Vector Machine based human activity recognition models. The study builds on both pre-existing and newly annotated data which includes interleaved activities. Results demonstrate that while Support Vector Machine based techniques perform well for all data sets considered, simple representations of sensor histories regularly outperform more complex count based models

A plan classifier based on Chi-square distribution tests

To make good decisions in a social context, humans often need to recognize the plan underlying the behavior of others, and make predictions based on this recognition. This process, when carried out by software agents or robots, is known as plan recognition, or agent modeling. Most existing techniques for plan recognition assume the availability of carefully hand-crafted plan libraries, which encode the a-priori known behavioral repertoire of the observed agents; during run-time, plan recognition algorithms match the observed behavior of the agents against the plan-libraries, and matches are reported as hypotheses. Unfortunately, techniques for automatically acquiring plan-libraries from observations, e.g., by learning or data-mining, are only beginning to emerge. We present an approach for automatically creating the model of an agent behavior based on the observation and analysis of its atomic behaviors. In this approach, observations of an agent behavior are transformed into a sequence of atomic behaviors (events). This stream is analyzed in order to get the corresponding behavior model, represented by a distribution of relevant events. Once the model has been created, the proposed approach presents a method using a statistical test for classifying an observed behavior. Therefore, in this research, the problem of behavior classification is examined as a problem of learning to characterize the behavior of an agent in terms of sequences of atomic behaviors. The experiment results of this paper show that a system based on our approach can efficiently recognize different behaviors in different domains, in particular UNIX command-line data, and RoboCup soccer simulationThis work has been partially supported by the Spanish Government under project TRA2007-67374-C02-0

A Fast Goal Recognition Technique Based on Interaction Estimates

Goal Recognition is the task of inferring an actor's goals given some or all of the actor's observed actions. There is considerable interest in Goal Recognition for use in intelligent personal assistants, smart environments, intelligent tutoring systems, and monitoring user's needs. In much of this work, the actor's observed actions are compared against a generated library of plans. Recent work by Ramirez and Geffner makes use of AI planning to determine how closely a sequence of observed actions matches plans for each possible goal. For each goal, this is done by comparing the cost of a plan for that goal with the cost of a plan for that goal that includes the observed actions. This approach yields useful rankings, but is impractical for real-time goal recognition in large domains because of the computational expense of constructing plans for each possible goal. In this paper, we introduce an approach that propagates cost and interaction information in a plan graph, and uses this information to estimate goal probabilities. We show that this approach is much faster, but still yields high quality results