We present a system that produces emotionally rich poetry inspired by personalized and empathic interpretation of text, particularly Internet blogs. Our implemented system is based on the blackboard architecture, and generates poetry from a theme that it considers the most inspiring. It also incorporates a model of emotions with an individual optimism rate that defines an affective state. The poems produced by the system contain emotional expressions that describe these feelings. We explain how the system re-conceptualizes the text by the empathic interpretation of its content. We also present how the blackboard architecture may support divergent problem solving in the field of computational creativity.We describe the system architecture and the generation algorithm followed by some illustrative results. Finally, we mention possible continuation of this work by incorporating other language generating systems as well as human experts in the blackboard architecture.

This paper describes the architecture of R&D Analyst, a commercial intelligent decision system for evaluating corporate research and development projects and portfolios. In analyzing projects, R&D Analyst interactively guides a user in constructing an influence diagram model for an individual research project. The system's interactive approach can be clearly explained from a blackboard system perspective. The opportunistic reasoning emphasis of blackboard systems satisfies the flexibility requirements of model construction, thereby suggesting that a similar architecture would be valuable for developing normative decision systems in other domains. Current research is aimed at extending the system architecture to explicitly consider of sequential decisions involving limited temporal, financial, and physical resources.

The emergence of the blackboard architecture as a widely used paradigm for problem solving led us and other members of the blackboard research community to organize a workshop. The workshop was held during the 1987 Association for the Advancement of Artificial Intelligence Conference in Seattle. The main purpose of the workshop was to highlight the advances in blackboard architectures since the introduction of the paradigm in Hearsay-II and identify issues relevant to future blackboard system research. This article describes the issues raised and the discussions in each of the five workshop panels.

The emergence of the blackboard architecture as a widely used paradigm for problem solving led us and other members of the blackboard research community to organize a workshop. The workshop was held during the 1987 Association for the Advancement of Artificial Intelligence Conference in Seattle. The main purpose of the workshop was to highlight the advances in blackboard architectures since the introduction of the paradigm in Hearsay-II and identify issues relevant to future blackboard system research. This article describes the issues raised and the discussions in each of the five workshop panels.

An important task in postal automation technology is determining the position and orientation of the destination address block in the image of a mail piece such as a letter, magazine, or parcel. The corresponding subimage is then presented to a human operator or a machine reader (optical character reader) that can read the zip code and, if necessary, other address information and direct the mail piece to the appropriate sorting bin. Analysis of physical characteristics of mail pieces indicates that in order to automate the address finding task, several different image analysis operations are necessary. Some examples are locating a rectangular white address label on a multicolor background, progressively grouping characters into text lines and text lines into text blocks, eliminating candidate regions by specialized detectors (for example, detecting regions such as postage stamps), and identifying handwritten regions. Described here are several operations, their utility as predicted by statistics of mail piece characteristics, and the results of applying the operations to a task set of mail piece images. A problem-solving architecture based on the blackboard model of problem solving for appropriately invoking the tools and combining their results is described.

Turbine Generator Diagnostics," see "Research in Artificial Intelligence at Osborne, Robert L. Tenenbaum, Jay M., see Pan, Jeff. the University of Pennsylvania."

The objectives of this document (a part of a retrospective monograph on the AGE Project currently in preparation) are (1) to define what is meant by blackboard systems and (2) to show the richness and diversity of blackboard system designs. In Part 1 we discussed the underlying concept behind all blackboard systems -- the blackboard model of problem solving. We also traced the history of ideas and designs of some application systems that helped shape the blackboard model. In application systems, the blackboard system components are integrated into the domain knowledge required to solve the problem at hand.

The first blackboard system was the Hearsay-II speech-understanding system (Erman et al. 1980), which evolved between 1971 and 1976. Subsequently, many systems have been built that have similar system organization and run-time behavior. The objectives of this document (a part of a retrospective monograph on the AGE Project currently in preparation) are (1) to define what is meant by blackboard systems and (2) to show the richness and diversity of blackboard system designs. In Part 1 we discussed the underlying concept behind all blackboard systems -- the blackboard model of problem solving. In order to bridge the gap between the model and working systems, we introduced and discussed the blackboard framework. We also traced the history of ideas and designs of some application systems that helped shape the blackboard model. In Part 2, we describe and contrast existing blackboard systems. Blackboard systems can generally be divided into two categories: application systems and skeletal systems. In application systems, the blackboard system components are integrated into the domain knowledge required to solve the problem at hand.

The objectives of this article are (1) to define what is meant by "blackboard systems" and (2) to show the richness and diversity of blackboard system designs. The article begins with a discussion of the underlying concept behind all blackboard systems, the blackboard model of problem solving. In Section 2 the history of ideas is traced, and the designs of some application systems that helped shape the blackboard model are detailed. Part 2 of this article which will appear in the next issue of AI Magazine, describes and contrasts some blackboard systems and discusses the characteristics of application problems suitable for the blackboard method of problem solving.

The first blackboard system was the HEARSAY-II speech understanding system (Erman et al.,1980) that evolved between 1971 and 1976. Subsequently, many systems have been built that have similar system organization and run-time behavior. The objectives of this article are (1) to define what is meant by "blackboard systems" and (2) to show the richness and diversity of blackboard system designs. The article begins with a discussion of the underlying concept behind all blackboard systems, the blackboard model of problem solving. In order to bridge the gap between a model and working systems, the blackboard framework, an extension of the basic blackboard model is introduced, including a detailed description of the model's components and their behavior. A model does not come into existence on its own, and is usually an abstraction of many examples. In Section 2 the history of ideas is traced, and the designs of some application systems that helped shape the blackboard model are detailed. Part 2 of this article which will appear in the next issue of AI Magazine, describes and contrasts some blackboard systems and discusses the characteristics of application problems suitable for the blackboard method of problem solving.