The email is used daily by millions of people to communicate around the globe and it is a mission-critical application for many businesses. Over the last decade, unsolicited bulk email has become a major problem for email users. An overwhelming amount of spam is flowing into users' mailboxes daily. In 2004, an estimated 62% of all email was attributed to spam. Spam is not only frustrating for most email users, it strains the IT infrastructure of organizations and costs businesses billions of dollars in lost productivity. In recent years, spam has evolved from an annoyance into a serious security threat, and is now a prime medium for phishing of sensitive information, as well the spread of malicious software. This work presents a first approach to attack the spam problem. We propose an algorithm that will improve a classifier's results by adjusting its training set data. It improves the document's vocabulary representation by detecting good topic descriptors and discriminators.

Despite the recent progress towards efficient multiple kernel learning (MKL), the structured output case remains an open research front. Current approaches involve repeatedly solving a batch learning problem, which makes them inadequate for large scale scenarios. We propose a new family of online proximal algorithms for MKL (as well as for group-lasso and variants thereof), which overcomes that drawback. We show regret, convergence, and generalization bounds for the proposed method. Experiments on handwriting recognition and dependency parsing testify for the successfulness of the approach.

This paper discusses the benefits of describing the world as information, especially in the study of the evolution of life and cognition. Traditional studies encounter problems because it is difficult to describe life and cognition in terms of matter and energy, since their laws are valid only at the physical scale. However, if matter and energy, as well as life and cognition, are described in terms of information, evolution can be described consistently as information becoming more complex. The paper presents eight tentative laws of information, valid at multiple scales, which are generalizations of Darwinian, cybernetic, thermodynamic, psychological, philosophical, and complexity principles. These are further used to discuss the notions of life, cognition and their evolution.

This special issue issue of AI Magazine presents six articles on some of the most interesting question answering systems in development today. Included are articles on Project, the Semantic Research, Watson, True Knowledge, and TextRunner (University of Washington’s clever use of statistical NL techniques to answer questions across the open web).

So, we have a variety of people here with different interests and backgrounds that I asked to talk about not just the key challenges ahead but potential opportunities and promising pathways, trajectories to solving those problems, and their predictions about how R&D might proceed in terms of the timing of various kinds of development over time. I asked the panelists briefly to frame their comments sharing a little bit about fundamental questions, such as, "What is the research goal?" Not everybody stays up late at night hunched over a computer or a simulation or a robotic system, pondering the foundations of intelligence and human-level AI. We have here today Lise Getoor from the University ipate the liability and insurance industry; and the of Maryland; Devika Subramanian, who other one, that it was a human interface problem, comes to us from Rice University; we have Carlos that people don't necessarily want to go and type Guestrin from Carnegie Mellon University (CMU); a bunch of yes/no questions into a computer to get James Hendler from Rensselaer Polytechnic Institute an answer, even with a rule-based explanation, (RPI); Mike Wellman at the University of that if you'd taken that just a step further and Michigan; Henry Kautz at tjhe University of solved the human problem, it might have worked. Rochester; and Joe Konstan, who comes to us from Related to that, I was remembering a bunch of the Midwest, as our Minneapolis person here on these smart house projects. And I have to admit I the panel. I think everyone Joe Konstan: I was actually surprised when you hates smart spaces. I think of myself at the core there's nobody there, do you warn people and give in human-computer interaction. So I went back them a chance to answer? There's no good answer and started looking at what I knew of artificial to this question. I can tell you if that person is in intelligence to try to see where the path forward bed asleep, the answer is no, don't wake them up was, and I was inspired by the past.

This paper describes our current approach in implementing computational improvisational micro-agents. This approach is intended to foster bottom-up research to better understand how to build more complex agent behaviors in a theatrical improvisational setting. Micro-agent designs are based on our current findings in a multi-year study focused on studying real life theatrical improvisers with an aim towards better understanding the cognition employed inimprovisation at the individual and group level. It also introduces a key architectural component from the domain of fuzzy logic that enables us to clearly represent some of our current findings.

The attrition game considered in this study is a graph based strategic game which is a movement-prohibited analogue of small-scale combat situations that arise frequently in popular real-time strategy video games. We present proofs that the attrition game, under a variety of assumptions, is a computationally hard problem in general. We also analyze the 1 vs. n unit case, for which we derive optimal target-orderings that can be computed in polynomial time and used as a core for heuristics for the general case. Finally, we present small problem instances that require randomizing moves — a fact that at first glance seems counter-intuitive.

A key challenge in computational narrative is story generation. In this paper we focus on analogy-based story generation, and, specifically, on how to generate both story and text using analogy. We present a dual representation formalism where a human-understandable representation (composed of English sentences) and a computer-understandable representation (consisting in a graph) are linked together in order to generate both story and natural language text by analogy. We have implemented our technique in the Riu interactive narrative system.

Recent years have seen a growing interest in interactive narrative systems that dynamically adapt story experiences in response to users’ actions, preferences, and goals. However, relatively little empirical work has investigated runtime models of user knowledge for informing interactive narrative adaptations. User knowledge about plot scenarios, story environments, and interaction strategies is critical in a range of interactive narrative contexts, such as mystery and detective genre stories, as well as narrative scenarios for education and training. This paper proposes a dynamic Bayesian network approach for modeling user knowledge in interactive narrative environments. A preliminary version of the model has been implemented for the Crystal Island interactive narrative-centered learning environment. Results from an initial empirical evaluation suggest several future directions for the design and evaluation of user knowledge models for guiding interactive narrative generation and adaptation.

First/third-person simulations in virtual environments have become increasingly used in training; however, creating intelligent, interactive characters to populate these environments presents a large authorial burden. Our work focuses on building tools to enable rapid creation of intelligent characters for first/third-person game-like environments with no programming knowledge required by the user. This is made possible using behavior-based control combined with a user interface employing natural language-like character specification in the form of English sentences and interactive testing during development.