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Image-based profiling has emerged as a powerful technology for various steps in basic biological and pharmaceutical discovery, but the community has lacked a large, public reference set of data from chemical and genetic perturbations. Here we present data generated by the Joint Undertaking for Morphological Profiling (JUMP)-Cell Painting Consortium, a collaboration between 10 pharmaceutical companies, six supporting technology companies, and two non-profit partners. When completed, the dataset will contain images and profiles from the Cell Painting assay for over 116,750 unique compounds, over-expression of 12,602 genes, and knockout of 7,975 genes using CRISPR-Cas9, all in human osteosarcoma cells (U2OS). The dataset is estimated to be 115 TB in size and capturing 1.6 billion cells and their single-cell profiles. File quality control and upload is underway and will be completed over the coming months at the Cell Painting Gallery:https://registry.opendata.aws/cellpainting-gallery. A portal to visualize a subset of the data is available athttps://phenaid.ardigen.com/jumpcpexplorer/.

InfoSleuth is an agent-based system that can be configured to perform many different information management activities in a distributed environment. InfoSleuthTM agents provide a number of complex query services that require resolving ontology-based queries over dynamically changing, distributed, heterogeneous resources. These include distributed query processing, location-independent single-resource updates, event and information monitoring, statistical or inferential data analysis, and trend discovery in complex event streams. It has been used in numerous applications, including the Environmental Data Exchange Network and the Competitive Intelligence System.

The goal of the InfoSleuth project at MCC is to exploit and synthesize new technologies into a unified system that retrieves and processes information in an ever-changing network of information sources. InfoSleuth has its roots in the Carnot project at MCC, which specialized in integrating heterogeneous information bases. However, recent emerging technologies such as internetworking and the World Wide Web have significantly expanded the types, availability, and volume of data available to an information management system. Furthermore, in these new environments, there is no formal control over the registration of new information sources, and applications tend to be developed without complete knowledge of the resources that will be available when they are run. Federated database projects such as Carnot that do static data integration do not scale up and do not cope well with this ever-changing environment. On the other hand, recent Web technologies, based on keyword search engines, are scalable but, unlike federated databases, are incapable of accessing information based on concepts. In this experience paper, we describe the architecture, design, and implementation of a working version of InfoSleuth. We show how InfoSleuth integrates new technological developments such as agent technology, domain ontologies, brokerage, and internet computing, in support of mediated interoperation of data and services in a dynamic and open environment. We demonstrate the use of information brokering and domain ontologies as key elements for scalability.

This chapter proposes a multiple-step backtracking mechanism to maintain a tradeoff between replanning and rigid backtracking for exception handling and recovery, thus enabling business process management (BPM) systems to operate robustly even in complex and dynamic environments. The concept of BDI (belief, desire and intention) agent is applied to model and construct the BPM system to inherit its advantages of adaptability and flexibility. Then, the flexible backtracking approach is introduced by utilizing the beneficial features of event-driven and means-end reasoning of BDI agents. Finally, we incorporate open nested transaction model to encapsulate plan execution and backtracking to gain the system level support of concurrency control and automatic recovery. With the ability of reasoning about task characteristics, our approach enables the system to find and commence a suitable plan prior to or in parallel with a compensation process when a failure occurs. This kind of computing allows us to achieve business goals efficiently in the presence of exceptions and failures.

Replanning, which discards all alternatives found in previous execution, and step-by-step backtracking, which iterates back through all previous selection decisions, are two extreme ends of exception handling in agent systems. We describe a new "jump" approach to maintaining a tradeoff between them. Choice points, which are maintained in a stack, are introduced to record all plans found by the agent's planning procedures. By iterating on the stack, and reasoning about plan characteristics, an agent can find and launch a suitable plan prior to or in parallel with a compensation process for the failed path, thus achieving its goals efficiently in the presence of exceptions. Our approach combines and utilizes several beneficial features of a BDI agent, such as its data structure and deliberation cycle, together with an open nested transaction system which supports architectural-level concurrency control and distribution management.

In an agent system, the ability to handle problems and recover from them is important in sustaining stability and providing robustness. We claim that execution logging is essential to support agent system robustness, and that agents should have architectural-level support for logging and recovery methods. We describe an infrastructure-level, default methodology for agent problem-handling, based on logging, and supported by declaratively encoding domain-specific knowledge related to changes in goal status and semantic compensations. Via logging, the approach allows repair of already-completed as well as current goals. We define a language, APLR, to support and constrain incremental specification of problem-handling information, with the agents' problem-handling behaviour increasing in so phistication as more knowledge is added to the system. The approach is implemented by mapping the methodology and domain knowledge to 3APL-like plan rules extended to support logging.

In agent systems, an agent's recovery from, execution problems is often complicated by constraints that are not present in a more traditional distributed, database systems environment. An analysis of agent-related crash recovery issues is presented, and requirements for achieving 'acceptable' agent crash recovery are discussed. Motivated by this analysis, a novel approach to managing agent recovery is presented. It utilises an event-and task-driven model for employing semantic compensation; task retries, and checkpointing. The compensation/retry model requires a situated model of action and failure, and provides the agent with an emergent unified, treatment of both crash recovery and run-time failure-handling. This approach helps the agent to recover acceptably from crashes and execution problems; improve system predictability; manage inter-task dependencies; and address the way in which exogenous events or crashes can trigger the need for a re-decomposition of a task. Agent architecture is then presented, which uses pair processing to leverage these recovery techniques and increase the agent's availability on crash restart.

Increasing expense of developing microelectronic manufacturing technology threatens to slow the growth of the electronics industry. This paper describes the progress we have made in developing methodologies and techniques to reduce the cost of designing microelectronic manufacturing flows. Our approach is to partition the task of process flow design into a number of abstraction levels and provide mechanisms to translate between these levels. This approach results in a top-down design methodology where requirements from higher levels of abstraction are successively reduced to lower abstraction levels, while meeting the constraints imposed by the lower levels. The paper enumerates the abstraction levels we have identified so far, and describes the translation mechanisms for a class of process design tasks: modification of an existing flow in response to change in performance requirements. Finally, we briefly describe a design environment that incorporates these ideas.

The MCC InfoSleuth/sup TM/ Project is an agent-based system for information gathering and analysis tasks performed over networks of autonomous information sources. A key motivation of the InfoSleuth system is that real information gathering applications require long-running monitoring and integration of information at various levels of abstraction. To this end, InfoSleuth agents enable a loose integration of technologies allowing: (1) extraction of semantic concepts from autonomous information sources; (2) registration and integration of semantically annotated information from diverse sources; and (3) temporal monitoring, information routing, and identification of trends appearing across sources in the information network. In this paper we discuss the agents in InfoSleuth applications and the goal-driven interaction patterns that enable them to dynamically organize and cooperate to perform integrated and temporal information-gathering tasks.

Publisher Summary This chapter discusses bias in planning and explanation-based learning. Bias is any basis for choosing one generalization over another, other than strict consistency with the observed training instances. One has taken seriously the notion of bias as a means of characterizing variations among planners. It is hypothesized that this would yield three benefits: (1) help organize and understand many of the concepts in planning, (2) reduce the computational requirements of planning, and (3) induce effective biases in learning. The first benefit has not yet led to a complete theory or taxonomy of planning methods, but it has led to the development of several orthogonal bias dimensions that provide fragments of organization over the space of methods. Regarding the second benefit, initial experiments with the six planners suggest monotonie trade-offs between completeness and efficiency as the bias dimensions are traversed from least to most restrictive. On the third benefit, the effects of changes in planning bias on bias in explanation-based learning were investigated with a case study in generalization to N.

During the course of the Microelectronics Manufacturing Science and Technology (MMST) program, a number of sensors have been integrated into various test‐bed plasma etch reactors with the goal of monitoring, diagnosing, and controlling these processes. These sensors include single wavelength and spectral ellipsometers for real‐time in situ etch rate and endpoint determination; a standard monochromator for etch rate and nonuniformity measurements; an eddy current sensor for incoming metal thickness control; a rf monitor for rf current and voltage diagnostics; and a scatterometry‐based critical dimension sensor for linewidth measurements. The full integration of these sensors turned out to be a complex and time‐consuming task including hardware, optical, software, material, and processing issues. Once integrated into the reactor, and the appropriate process modeling completed, these sensors enabled the monitoring, diagnosis, and model‐based control of these processes. Besides maintaining specific process ob...

Soar is an architecture for intelligence that integrates learning into all of its problem-solving behavior. The learning mechanism, chunking, has been studied experimentally in a broad range of tasks and situations. This paper summarizes the research on chucking in Soar, covering the effects of chunking in different tasks, task-independent applications of chunking and our theoretical analyses of effects and limits of chunking. We discuss what and when Soar has been able to learn so far. The results demonstrate that the variety of learning in Soar arises from variety in problem solving, rather than from variety in architectural mechanisms. Keywords: Artificial intelligence, Machine learning, cognitive architecture.

In this article we describe five projects in progress on learning in the Soar architecture: (1) problem-space creation, (2) learning of macro-operators, (3) data chunking, (4) generalization and analogy, and (5) abstraction planning. We also briefly review Soar itself and its learning mechanism chunking.