The Temporal Coding Hypothesis of Miller and colleagues [7] suggests thatanimals integrate related temporal patterns of stimuli into single memory representations. We formalize this concept using quasi-Bayes estimation to update the parameters of a constrained hiddenMarkov model. This approach allows us to account for some surprising temporal effects in the second order conditioning experimentsof Miller et al. [1, 2, 3], which other models are unable to explain. 1 Introduction Animal learning involves more than just predicting reinforcement. The well-known phenomena of latent learning and sensory preconditioning indicate that animals learn about stimuli in their environment before any reinforcement is supplied. More recently, a series of experiments by R. R. Miller and colleagues has demonstrated that in classical conditioning paradigms, animals appear to learn the temporal structure ofthe stimuli [8].

The Association for the Advancement of Artificial Intelligence (AAAI) presented the AAAI-02 Workshop Program on Sunday and Monday, 28-29 July 2002 at the Shaw Convention Center in Edmonton, Alberta, Canada. The AAAI-02 workshop program included 18 workshops covering a wide range of topics in AI. The workshops were Agent-Based Technologies for B2B Electronic-Commerce; Automation as a Caregiver: The Role of Intelligent Technology in Elder Care; Autonomy, Delegation, and Control: From Interagent to Groups; Coalition Formation in Dynamic Multiagent Environments; Cognitive Robotics; Game-Theoretic and Decision-Theoretic Agents; Intelligent Service Integration; Intelligent Situation-Aware Media and Presentations; Meaning Negotiation; Multiagent Modeling and Simulation of Economic Systems; Ontologies and the Semantic Web; Planning with and for Multiagent Systems; Preferences in AI and CP: Symbolic Approaches; Probabilistic Approaches in Search; Real-Time Decision Support and Diagnosis Systems; Semantic Web Meets Language Resources; and Spatial and Temporal Reasoning.

The infrastructure of modern society is controlled by software systems. These systems are vulnerable to attacks; several such attacks, launched by "recreation hackers," have already led to severe disruption. However, a concerted and planned attack whose goal is to reap harm could lead to catastrophic results (for example, by disabling the computers that control the electrical power grid for a sustained period of time). The survivability of such information systems in the face of attacks is therefore an area of extreme importance to society. This article is set in the context of self-adaptive survivable systems: software that judges the trustworthiness of the computational resources in its environment and that chooses how to achieve its goals in light of this trust model. Each self-adaptive survivable system detects and diagnoses compromises of its resources, taking whatever actions are necessary to recover from attack. In addition, a long-term monitoring system collects evidence from intrusion detectors, firewalls, and all the selfadaptive components, building a composite trust model used by each component. Self-adaptive survivable systems contain models of their intended behavior; models of the required computational resources; models of the ways in which these resources can be compromised; and finally, models of the ways in which a system can be attacked and how such attacks can lead to compromises of the computational resources. In this article, I focus on computational vulnerability analysis: a system that, given a description of a computational environment, deduces all the attacks that are possible. In particular, its goal is to develop multistage attack models in which the compromise of one resource is used to facilitate the compromise of other, more valuable resources. Although the ultimate aim is to use these models online as part of a self-adaptive system, there are other offline uses as well that we are deploying first to help system administrators assess the vulnerabilities of their computing environment.

Delivering effective customer service over the internet requires attention to many aspects of knowledge management if it is to be both satisfying for customers and economical for the company or other organization. In RightNow ESERVICE CENTER, such management is built into the architecture and supported by automatically gathering metainformation about the documents held in the core knowledge base. A variety of AI techniques are used to facilitate the construction, maintenance, and navigation of the knowledge base. These techniques include collaborative filtering, swarm intelligence, fuzzy logic, natural language processing, text clustering, and classification rule learning. Customers using ESERVICE CENTER report dramatic decreases in support costs and increases in customer satisfaction because of the ease of use provided by the self-learning features of the knowledge base.

The Fourteenth Innovative Applications of Artificial Intelligence Conference (IAAI-2002) was held from 28 July to 1 August in Edmonton, Alberta, Canada, in conjunction with the Seventeenth National Conference on Artificial Intelligence (AAAI-2002). As in past years, papers were solicited in two categories: (1) deployed applications and (2) emerging applications and technologies. Deployed application papers describe systems that have been in use for at least several months by individuals or organizations other than their developers, have measurable benefits, and incorporate AI technologies. Emerging applications are technologies and systems that are close to deployment and clearly show an innovative implementation of AI technologies. These papers are of value not only to other application developers looking for guidance in applying various techniques to their own applications but also to researchers who need to understand the unique technical challenges provided by real-world problems.

Send applications and inquiries to May Cheh; National Library of Medicine, 8600 Rockville Pike, Mail Stop 54, Bethesda, MD 20894-6075; Email: cheh@nlm.nih.gov

February 10: IJCAI-03 Electronic After the conference, an expense report Mark your calendars now for IJCAI-poster submission deadline will be required to account for the 03! The Eighteenth International February 12: IJCAI-03 Hard-copy funds awarded.

To them that had had, more would be given (Lodge 1986, p. 172). "Morris read through the letter. Was it a shade too fulsome? No, that was another law of academic life: it is impossible to be excessive in the flattery of one's peers." There we met Morris That book was made by Mr. Mark I read these lines as a new truth." I haven't even gotten my Who is talking floor, and stepped out on to his regular on the British version of the here? More importantly, whom balcony to inhale the air, scented Discovery Channel), and womanizer should I believe? Messenger, as his wife Twain" disguised as Huck?

The evolution of the World Wide Web from a repository of HTML data to a source of varied distributed services creates exciting opportunities for offering complex, integrated services over the web. The syntactic problems of such integration are being addressed by the advent of the web services stack of standards.1 However, the promise of service integration will not be delivered unless services can be integrated semantically as well. The 2002 AAAI workshop entitled "Intelligent Service Integration" examined this new challenge for the AI community.

The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University's Department of Computer Science, presented the 2002 Spring Symposium Series, held Monday through Wednesday, 25 to 27 March 2002, at Stanford University. The nine symposia were entitled (1) Acquiring (and Using) Linguistic (and World) Knowledge for Information Access; (2) Artificial Intelligence and Interactive Entertainment; (3) Collaborative Learning Agents; (4) Information Refinement and Revision for Decision Making: Modeling for Diagnostics, Prognostics, and Prediction; (5) Intelligent Distributed and Embedded Systems; (6) Logic-Based Program Synthesis: State of the Art and Future Trends; (7) Mining Answers from Texts and Knowledge Bases; (8) Safe Learning Agents; and (9) Sketch Understanding.