Gaussian Processes provide good prior models for spatial data, but can be too smooth. In many physical situations there are discontinuities along bounding surfaces, for example fronts in near-surface wind fields. We describe a modelling method for such a constrained discontinuity and demonstrate how to infer the model parameters in wind fields with MCMC sampling.

John Deere (Moline, Ill.), a manufacturer of agricultural and industrial equipment, has adopted a genetic algorithm-based solution to solve its factory scheduling problems. John Deere is using genetic algorithms to streamline scheduling at its factories, Sarasota County Detention Center knowledge in a system model and balancing an increasing number of (Sarasota, Fla.) has incorporated The center will use the The Royal Sonesta Hotel Boston Martin Marietta Magnesia Specialties new system to identify and confirm (Cambridge, Mass.) has deployed a (Woodville, Ohio), a producer of identities of inmates prior to being speech-driven automated attendant magnesia chemicals for industrial released from the facility. LucasArts Entertainment (San technology to automatically answer Primary objectives for the system Rafael, Calif.) has deployed a casebased and direct telephone calls, enabling are to increase production yet maintain reasoning self-service customer each caller direct access to a registered quality and decrease energy costs. This Report (Cuyahoga Falls, Ohio; Shanghai PuDong International Airport resource-allocation application evaluates www.lionhrtpub.com), The react to unforeseen events in real time.

Interacting with a computer requires adopting some metaphor to guide our actions and expectations. Most human-computer interfaces can be classified according to two dominant metaphors: (1) agent and (2) environment. Interactions based on an agent metaphor treat the computer as an intermediary that responds to user requests. In the environment metaphor, a model of the task domain is presented for the user to interact with directly. The term agent has come to refer to the automation of aspects of human-computer interaction (HCI), such as anticipating commands or autonomously performing actions. Norman's 1984 model of HCI is introduced as reference to organize and evaluate research in human-agent interaction (HAI). A wide variety of heterogeneous research involving HAI is shown to reflect automation of one of the stages of action or evaluation within Norman's model. Improvements in HAI are expected to result from a more heterogeneous use of methods that target multiple stages simultaneously.

Agent-based systems technology has generated lots of excitement in recent years because of its promise as a new paradigm for conceptualizing, designing, and implementing software systems. This promise is particularly attractive for creating software that operates in environments that are distributed and open, such as the internet. Currently, the great majority of agent-based systems consist of a single agent. However, as the technology matures and addresses increasingly complex applications, the need for systems that consist of multiple agents that communicate in a peer-to-peer fashion is becoming apparent. Central to the design and effective operation of such multiagent systems (MASs) are a core set of issues and research questions that have been studied over the years by the distributed AI community. In this article, I present some of the critical notions in MASs and the research work that has addressed them. I organize these notions around the concept of problem-solving coherence, which I believe is one of the most critical overall characteristics that an MAS should exhibit.

Deneb Robotics (Auburn Hills, Mich.) has been awarded a $2.3 million contract from the National Institute of Standards and Technology (NIST) to develop the agent network for task scheduling and execution. This intelligent agent-based project is designed to improve existing factory-scheduling systems with a new task scheduling and execution system in which Shell U.K. Exploration and Production availability and prevent cars from agents represent factory resources, systems, (Aberdeen, U.K.) has implemented being damaged while they are parked. The Arvin Industries (Columbus, Ind.) is Cisco Systems (San Jose, Calif.), a supplier expert system helped Shell achieve working with the U.S. Air Force to of network technology, is using over $1.6 million in cost savings for develop a neural network system that intelligent-agent technology to integrate its Brent Field site within 2 months of can determine the quality of noise in CD-ROM and online web information implementation. The neural network will help The addition of intelligent The National Research Council has determine what exactly an annoying search-and-retrieval capabilities has awarded Nestor (Providence, R.I.) a sound is and how it can be fixed. Mercedes-Benz plans This system has helped cut specialty Neural Computer Sciences (NCS) to establish three vrf test sites in clinic costs by 40 percent.

Kukjin Kang and Jong-Hoon Oh Department of Physics Pohang University of Science and Technology Hyoja San 31, Pohang, Kyongbuk 790-784, Korea Email: kkj.jhohOgalaxy.postech.ac.kr Abstract We study generalization capability of the mixture of experts learning fromexamples generated by another network with the same architecture. When the number of examples is smaller than a critical value,the network shows a symmetric phase where the role of the experts is not specialized. Upon crossing the critical point, the system undergoes a continuous phase transition to a symmetry breakingphase where the gating network partitions the input space effectively and each expert is assigned to an appropriate subspace. Wealso find that the mixture of experts with multiple level of hierarchy shows multiple phase transitions. 1 Introduction Recently there has been considerable interest among neural network community in techniques that integrate the collective predictions of a set of networks[l, 2, 3, 4]. The mixture of experts [1, 2] is a well known example which implements the philosophy ofdivide-and-conquer elegantly.

The Self-Organizing Map (SOM) algorithm has been extensively studied and has been applied with considerable success to a wide variety of problems. However, the algorithm is derived from heuristic ideasand this leads to a number of significant limitations. In this paper, we consider the problem of modelling the probability densityof data in a space of several dimensions in terms of a smaller number of latent, or hidden, variables. We introduce a novel form of latent variable model, which we call the GTM algorithm (forGenerative Topographic Mapping), which allows general nonlinear transformations from latent space to data space, and which is trained using the EM (expectation-maximization) algorithm. Ourapproach overcomes the limitations of the SOM, while introducing no significant disadvantages. We demonstrate the performance ofthe GTM algorithm on simulated data from flow diagnostics for a multiphase oil pipeline.

The Self-Organizing Map (SOM) algorithm has been extensively studied and has been applied with considerable success to a wide variety of problems. However, the algorithm is derived from heuristic ideas and this leads to a number of significant limitations. In this paper, we consider the problem of modelling the probability density of data in a space of several dimensions in terms of a smaller number of latent, or hidden, variables. We introduce a novel form of latent variable model, which we call the GTM algorithm (for Generative Topographic Mapping), which allows general nonlinear transformations from latent space to data space, and which is trained using the EM (expectation-maximization) algorithm. Our approach overcomes the limitations of the SOM, while introducing no significant disadvantages. We demonstrate the performance of the GTM algorithm on simulated data from flow diagnostics for a multiphase oil pipeline.

The Self-Organizing Map (SOM) algorithm has been extensively studied and has been applied with considerable success to a wide variety of problems. However, the algorithm is derived from heuristic ideas and this leads to a number of significant limitations. In this paper, we consider the problem of modelling the probability density of data in a space of several dimensions in terms of a smaller number of latent, or hidden, variables. We introduce a novel form of latent variable model, which we call the GTM algorithm (for Generative Topographic Mapping), which allows general nonlinear transformations from latent space to data space, and which is trained using the EM (expectation-maximization) algorithm. Our approach overcomes the limitations of the SOM, while introducing no significant disadvantages. We demonstrate the performance of the GTM algorithm on simulated data from flow diagnostics for a multiphase oil pipeline.

Closed-loop control relies on sensory feedback that is usually assumed to be free. But if sensing incurs a cost, it may be costeffective to take sequences of actions in open-loop mode. We describe a reinforcement learning algorithm that learns to combine open-loop and closed-loop control when sensing incurs a cost. Although we assume reliable sensors, use of open-loop control means that actions must sometimes be taken when the current state of the controlled system is uncertain. This is a special case of the hidden-state problem in reinforcement learning, and to cope, our algorithm relies on short-term memory. The main result of the paper is a rule that significantly limits exploration of possible memory states by pruning memory states for which the estimated value of information is greater than its cost. We prove that this rule allows convergence to an optimal policy.