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five events. These movies were processed by LEONARD.

8 The results of this preliminary evaluation are summarized in Table 1. A more extensive evaluation of LEONARD will be conducted in the future.

## 6. Discussion

This paper presents a new approach to event recognition that differs from the prior approach in two ways. First, it uses force dynamics instead of motion profile as the feature set to differentiate between event types. Second, it uses event logic instead of hidden Markov models as the computational framework for classifying time-series data containing these features. Nominally, these two differences are independent. One can imagine using hidden Markov models to classify time series of force-dynamic features or using event logic to classify time series of motion-profile features.

While such combinations are feasible in principle, they are unwieldy in practice.

Consider using event logic to classify time series of motion-profile features. Motion-profile features, such as position, velocity, and acceleration, are typically continuous. A given event usually corresponds to a vague range of possible feature values. This vagueness is well modeled by continuous-output hidden Markov models. Event logic, which is discrete in nature, requires quantizing precise feature-value ranges. Such quantization can lead to a high misclassification rate.

Furthermore, continuous distributions allow partitioning a multidimensional feature space into different classes where the boundaries between classes are more complex than lines along the feature axes. Emulating this in event logic would require complex disjunctive expressions.

Similarly, consider using hidden Markov models to classify time series of force-dynamic features. Suppose that a feature vector contains n features. Since both force-dynamic and motionprofile features typically relate pairs of objects, n is often quadratic in the number of event par-