Imagine an Internet-scale knowledge system where people and intelligent agents can collaborate on solving complex problems in business, engineering, science, medicine, and other endeavors. Its resources include semantically tagged websites, wikis, and blogs, as well as social networks, vertical search engines, and a vast array of web services from business processes to AI planners and domain models. Research prototypes of decentralized knowledge systems have been demonstrated for years, but now, thanks to the web and Moore's law, they appear ready for prime time. This article introduces the architectural concepts for incrementally growing an Internet-scale knowledge system and illustrates them with scenarios drawn from e-commerce, e-science, and e-life.

This is a wonderful opportunity, yet a position is very hard to match in any other. The first AAAI conference was held at Stanford University; it was very much a research conference, a scientific event that generated a lot of excitement. The conference was small and intimate, with few parallel sessions. There were excellent opportunities for us to talk to one another. AAAI-80 gave real substance to the organization, clearly getting AAAI off on the right foot, and it gave new identity and cohesiveness to the field. This year--2006--has also been a big year, celebrating the 50th anniversary of the original meeting at Dartmouth College, where the name "artificial intelligence" first came into common use. Numerous events around the world, including a celebratory symposium at Dartmouth and an AAAI Fellows Symposium associated with AAAI-05, have marked this important milestone in the history of the field. Progress since our first AAAI conference has The First AAAI Conference was Held at Stanford University. While each year's results may have seemed incremental, when we look back over the entire period we see some truly amazing plate the big picture and, perhaps more importantly things. In job at DARPA), to identify gaps in our national hindsight this may no longer look so exciting computing research agenda. It also occurred to (purists will say that it was not an "AI" system me that that perspective was a very special that beat Garry Kasparov but rather a highly asset to use in drafting this presidential engineered special-purpose machine largely address. Looking forward from back then, no want to raise a broad issue and consider matter how Deep Blue actually worked, playing some larger questions regarding the nature of chess well was clearly an AI problem--in fact, a the field itself and the role that AAAI as an classical one--and our success was historic.

What Do We Know about Knowledge? In this article, I will examine the first of these questions. AI has been slow to embrace this principle. Programs demonstrating research ideas in AI are often too large and not well enough documented to allow replication or sharing. What I would like to in diverse conditions. I wish to clarify the knowledge example, it was pretty clearly articulated in Biblical principle and try to increase our understanding times: "A man of knowledge increaseth of what programmers and program strength" (Proverbs 24: 5). Greek philosophers based their lives on acquiring The "knowledge is power" principle is most and transferring knowledge. In the course closely associated with Francis Bacon, from his of teaching, they sought to understand the 1597 tract on heresies: "Nam et ipsa scientia nature of knowledge and how we can establish potestas est." ("In and of itself, knowledge is knowledge of the natural world. B," along with quantification, "All A's are B's," Euclid's geometry firmly established the concept In the intervening several centuries before Plato, Socrates's pupil and Aristotle's mentor, was the first to pose the question in writing of the Middle Ages and the rise of modern science what we mean when we say that a person in the West, He was distinguishing empirical knowledge, church to make new knowledge fit with established lacking complete certainty, from the certain dogma.

The 1956 Dartmouth summer research project on artificial intelligence was initiated by this August 31, 1955 proposal, authored by John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon. The original typescript consisted of 17 pages plus a title page. Copies of the typescript are housed in the archives at Dartmouth College and Stanford University. The first 5 papers state the proposal, and the remaining pages give qualifications and interests of the four who proposed the study. In the interest of brevity, this article reproduces only the proposal itself, along with the short autobiographical statements of the proposers.

What Do We Know About Knowledge? Cover: AI@50--We Are Golden, by James Gary, New York, New York. Please contact AAAI for such permission. Electronic submissions should be submitted using the web-based submissions form. Submissions information is available at aimagazine.org.

Current developments in molecular electronics, motors and chemical sensors could enable constructing large numbers of devices able to sense, compute and act in micron-scale environments. Such microscopic machines, of sizes comparable to bacteria, could simultaneously monitor entire populations of cells individually in vivo. This paper reviews plausible capabilities for microscopic robots and the physical constraints due to operation in fluids at low Reynolds number, diffusion-limited sensing and thermal noise from Brownian motion. Simple distributed controls are then presented in the context of prototypical biomedical tasks, which require control decisions on millisecond time scales. The resulting behaviors illustrate trade-offs among speed, accuracy and resource use. A specific example is monitoring for patterns of chemicals in a flowing fluid released at chemically distinctive sites. Information collected from a large number of such devices allows estimating properties of cell-sized chemical sources in a macroscopic volume. The microscopic devices moving with the fluid flow in small blood vessels can detect chemicals released by tissues in response to localized injury or infection. We find the devices can readily discriminate a single cell-sized chemical source from the background chemical concentration, providing high-resolution sensing in both time and space. By contrast, such a source would be difficult to distinguish from background when diluted throughout the blood volume as obtained with a blood sample.

In this paper we present pddl+, a planning domain description language for modelling mixed discrete-continuous planning domains. We describe the syntax and modelling style of pddl+, showing that the language makes convenient the modelling of complex time-dependent effects. We provide a formal semantics for pddl+ by mapping planning instances into constructs of hybrid automata. Using the syntax of HAs as our semantic model we construct a semantic mapping to labelled transition systems to complete the formal interpretation of pddl+ planning instances. An advantage of building a mapping from pddl+ to HA theory is that it forms a bridge between the Planning and Real Time Systems research communities. One consequence is that we can expect to make use of some of the theoretical properties of HAs. For example, for a restricted class of HAs the Reachability problem (which is equivalent to Plan Existence) is decidable. pddl+ provides an alternative to the continuous durative action model of pddl2.1, adding a more flexible and robust model of time-dependent behaviour.

Active learners alleviate the burden of labeling large amounts of data by detecting and asking the user to label only the most informative examples in the domain. We focus here on active learning for multi-view domains, in which there are several disjoint subsets of features (views), each of which is sufficient to learn the target concept. In this paper we make several contributions. First, we introduce Co-Testing, which is the first approach to multi-view active learning. Second, we extend the multi-view learning framework by also exploiting weak views, which are adequate only for learning a concept that is more general/specific than the target concept. Finally, we empirically show that Co-Testing outperforms existing active learners on a variety of real world domains such as wrapper induction, Web page classification, advertisement removal, and discourse tree parsing.

In this paper we propose a data intensive approach for inferring sentence-internal temporal relations. Temporal inference is relevant for practical NLP applications which either extract or synthesize temporal information (e.g., summarisation, question answering). Our method bypasses the need for manual coding by exploiting the presence of markers like ``after", which overtly signal a temporal relation. We first show that models trained on main and subordinate clauses connected with a temporal marker achieve good performance on a pseudo-disambiguation task simulating temporal inference (during testing the temporal marker is treated as unseen and the models must select the right marker from a set of possible candidates). Secondly, we assess whether the proposed approach holds promise for the semi-automatic creation of temporal annotations. Specifically, we use a model trained on noisy and approximate data (i.e., main and subordinate clauses) to predict intra-sentential relations present in TimeBank, a corpus annotated rich temporal information. Our experiments compare and contrast several probabilistic models differing in their feature space, linguistic assumptions and data requirements. We evaluate performance against gold standard corpora and also against human subjects.

We develop a novel algorithm, called VIP*, for structured variational approximate inference. This algorithm extends known algorithms to allow efficient multiple potential updates for overlapping clusters, and overcomes the difficulties imposed by deterministic constraints. The algorithm's convergence is proven and its applicability demonstrated for genetic linkage analysis.