This paper describes a system that can annotate a video sequence with: a description of the appearance of each actor; when the actor is in view; and a representation of the actor's activity while in view. The system does not require a fixed background, and is automatic. The system works by (1) tracking people in 2D and then, using an annotated motion capture dataset, (2) synthesizing an annotated 3D motion sequence matching the 2D tracks. The 3D motion capture data is manually annotated off-line using a class structure that describes everyday motions and allows motion annotationsto be composed -- one may jump while running, for example. Descriptions computed from video of real motions show that the method is accurate.

We consider the general problem of learning from labeled and unlabeled data, which is often called semi-supervised learning or transductive inference. Aprincipled approach to semi-supervised learning is to design a classifying function which is sufficiently smooth with respect to the intrinsic structure collectively revealed by known labeled and unlabeled points. We present a simple algorithm to obtain such a smooth solution. Our method yields encouraging experimental results on a number of classification problemsand demonstrates effective use of unlabeled data.

This paper presents a method for learning a distance metric from relative comparisonsuch as "A is closer to B than A is to C". Taking a Support Vector Machine (SVM) approach, we develop an algorithm that provides a flexible way of describing qualitative training data as a set of constraints. We show that such constraints lead to a convex quadratic programming problem that can be solved by adapting standard methods forSVM training. We empirically evaluate the performance and the modelling flexibility of the algorithm on a collection of text documents.

We compute approximate analytical bootstrap averages for support vector classificationusing a combination of the replica method of statistical physics and the TAP approach for approximate inference. We test our method on a few datasets and compare it with exact averages obtained by extensive Monte-Carlo sampling.

Discrete Fourier transforms and other related Fourier methods have been practically implementable due to the fast Fourier transform (FFT). However there are many situations where doing fast Fourier transforms without complete data would be desirable. In this paper itis recognised that formulating the FFT algorithm as a belief network allows suitable priors to be set for the Fourier coefficients. Furthermore efficient generalised belief propagation methods between clustersof four nodes enable the Fourier coefficients to be inferred and the missing data to be estimated in near to O(n log n) time, where n is the total of the given and missing data points. This method is compared with a number of common approaches such as setting missing data to zero or to interpolation. It is tested on generated data and for a Fourier analysis of a damaged audio signal.

Joseph then to students who are actively February 19: IS Demos proposals E. Beck (joseph.beck@cmu.edu)

Satellite domains are becoming a fashionable area of research within the AI community due to the complexity of the problems that satellite domains need to solve. With the current U.S. and European focus on launching satellites for communication, broadcasting, or localization tasks, among others, the automatic control of these machines becomes an important problem. Many new techniques in both the planning and scheduling fields have been applied successfully, but still much work is left to be done for reliable autonomous architectures. The purpose of this article is to present CONSAT, a real application that plans and schedules the performance of nominal operations in four satellites during the course of a year for a commercial Spanish satellite company, HISPASAT. For this task, we have used an AI domain-independent planner that solves the planning and scheduling problems in the HISPASAT domain thanks to its capability of representing and handling continuous variables, coding functions to obtain the operators' variable values, and the use of control rules to prune the search. We also abstract the approach in order to generalize it to other domains that need an integrated approach to planning and scheduling.

We present a methodology for designing and implementing interactive intelligences. The constructionist design methodology (CDM) -- so called because it advocates modular building blocks and incorporation of prior work -- addresses factors that we see as key to future advances in AI, including support for interdisciplinary collaboration, coordination of teams, and large-scale systems integration. We test the methodology by building an interactive multifunctional system with a real-time perception- action loop. The system, whose construction relied entirely on the methodology, consists of an embodied virtual agent that can perceive both real and virtual objects in an augmented-reality room and interact with a user through coordinated gestures and speech. Wireless tracking technologies give the agent awareness of the environment and the user's speech and communicative acts. User and agent can communicate about things in the environment, their placement, and their function, as well as about more abstract topics, such as current news, through situated multimodal dialogue. The results demonstrate the CDM's strength in simplifying the modeling of complex, multifunctional systems that require architectural experimentation and exploration of unclear subsystem boundaries, undefined variables, and tangled data flow and control hierarchies.

Therefore, we are needed even if Edward Tsang's book

(Niles and Pease 2001). Considering that tremendous scheduling that are robust in the face of realworld progress has been made in commonsense reasoning concerns like time zones, daylight savings in specialized topics such as thermodynamics time, and international calendar variations. in physical systems (Collins and Forbus 1989), it is surprising that our best content theories Given the importance of an ontology of of people are still struggling to get past time across so many different commonsense simple notions of belief and intentionality (van der Hoek and Wooldridge 2003). However, search is the generation of competency theories systems that can successfully reason about that have a degree of depth necessary to solve people are likely to be substantially more valuable inferential problems that people are easily able than those that reason about thermodynamics to handle. in most future applications. Yet competency in content theories is only Content theories for reasoning about people half of the challenge. Commonsense reasoning are best characterized collectively as a theory of in AI theories will require that computers not commonsense psychology, in contrast to those only make deep humanlike inferences but also that are associated with commonsense (naïve) ensure that the scope of these inferences is as physics. The scope of commonsense physics, broad as humans can handle, as well. That is, best outlined in Patrick Hayes's first and second in addition to competency, content theories will "Naïve Physics Manifestos" (Hayes 1979, need adequate coverage over the full breadth of 1984), includes content theories of time, space, concepts that are manipulated in human-level physical entities, and their dynamics. It is only by achieving psychology, in contrast, concerns all some adequate level of coverage that we of the aspects of the way that people think they can begin to construct reasoning systems that think. It should include notions of plans and integrate fully into real-world AI applications, goals, opportunities and threats, decisions and where pragmatic considerations and expressive preferences, emotions and memories, along user interfaces raise the bar significantly.