Similar systems are being installed at other Texaco sites. Lear Astronics (Santa Monica and Ontario, Calif.) is combining neural networks with virtual reality to enhance its Autonomous Landing Guidance (ALG) system. Lear Astronics is using a neural network-based massively parallel coprocessor for real-time image processing in the ALG system, which enables commercial and military aircraft pilots to land in foggy conditions. Researchers at Georgia Tech (Atlanta, Ga.) have created intelligent agent software called the Technology Opportunities Analysis Knowbot (TOAK) that provides profiles of the latest technological trends and opportunities. TOAK navigates through multiple networks and across diverse computer systems to perform specific search tasks for the user.

This path, however, is typically not a shortest path in the continuous terrain. In this overview article, we discuss a path-planning methodology for quickly finding paths in continuous terrain that are typically shorter than shortest grid paths. Anyangle path-planning algorithms are variants of the heuristic path-planning algorithm A* that find short paths by propagating information along grid edges (like A*, to be fast) without constraining the resulting paths to grid edges (unlike A*, to find short paths). In robotics and video games, (continuous) terrain is often discretized into grids with blocked and unblocked grid cells and from there into grid graphs (Tozour 2004; Rabin 2000; Chrpa and Komenda 2011; Björnsson et al. 2003; Nash 2012). Our objective is to find short unblocked paths from given start vertices to given goal vertices.

A key feature of the AAMAS conference is its emphasis on ties to real-world applications. The focus of this article is to provide a broad overview of application-focused papers published at the AAMAS 2010 and 2011 conferences. More specifically, recent applications at AAMAS could be broadly categorized as belonging to research areas of security, sustainability, and safety. We outline the domains of applications, key research thrusts underlying each such application area, and emerging trends. This emphasis of trying to marry theory and practice at AAMAS goes all the way back to the origins of its predecessor conferences, such as the first International Conference on Autonomous Agents (Johnson 1997).

We describe an intelligent personal assistant that has been developed to aid a busy knowledge worker in managing time commitments and performing tasks. The design of the system was motivated by the complementary objectives of (1) relieving the user of routine tasks, thus allowing her to focus on tasks that critically require human problem-solving skills, and (2) intervening in situations where cognitive overload leads to oversights or mistakes by the user. The system draws on a diverse set of AI technologies that are linked within a Belief-Desire- Intention (BDI) agent system. Although the system provides a number of automated functions, the overall framework is highly user centric in its support for human needs, responsiveness to human inputs, and adaptivity to user working style and preferences. While doing so, she must maintain awareness of deadlines and resources, as well as tracking current activities and new information that could affect her objectives and productivity.

We then describe three broad areas of current inquiry by AI researchers in algorithmic game theory: game playing, social choice, and mechanism design. Finally, we give short summaries of each of the six articles appearing in this issue. The field took on its modern form in the 1940s and 1950s (von Neumann and Morgenstern 1947; Nash 1950, Kuhn 1953), with even earlier antecedents (such as Zermelo 1913 and von Neumann 1928). Although it has had occasional and significant overlap with computer science over the years, game theory received most of its early study by economists. Indeed, game theory now serves as perhaps the main analytical framework in microeconomic theory, as evidenced by its prominent role in economics textbooks (for example, Mas-Colell, Whinston, and Green 1995) and by the many Nobel prizes in economic sciences awarded to prominent game theorists.

AI's War on Manipulation: Are We Winning? The next day was going to be a big day: Citizens of Bitotia would once and for all establish which byte order was better, big-endian (B) or little-endian (L). Little Bit Timmy was a big supporter of little endian because that would give him the best position in the word. However, the population was split quite evenly between L and B, with a small minority of Bits who still remembered the single-tape Turing machine and preferred unary encoding (U), without any of this endianness business. Nonetheless, about half of the Bits preferred big-endian (B L U), and about half were the other way round (L B U).

This article is a reflection on agent-based AI. My contention is that AI research should focus on interactive, autonomous systems, that is, agents. We see how recent developments in (multi-) agent-oriented research have taken us closer to the original AI goal, namely, to build intelligent systems of general competence. Agents are not the panacea though. I point out several areas such as design description, implementation, reusability, and security that must be developed before agents are universally accepted as the AI of the future.

After a general discussion about modeling and simulation, we address the basic concept of ABMS, focusing on its generative and bottom-up nature, its advantages as well as its pitfalls. The subsequent part of the article deals with application-oriented aspects, including selected tools and well-known applications. In order to illustrate the benefits of using ABMS, we focus on several aspects of a well-known area related to simulation of complex systems, namely traffic. At the end, a brief look into future challenges is given. Was this caused by climate changes?

With the growing importance of multiagent teamwork, tools that can help humans analyze, evaluate, and understand team behaviors are also becoming increasingly important. To this end, we are creating isaac, a team analyst agent for post hoc, offline agent-team analysis. With the growing importance of teamwork, there is now a critical need for tools to help humans analyze, evaluate, and understand team behaviors. Indeed, in multiagent domains with tens or even hundreds of agents in teams, agent interactions are often highly complex and dynamic, making it difficult for human developers to analyze agent-team behaviors. The problem is further exacerbated in environments where agents are developed by different developers, where even the intended interactions are unpredictable.

The need to save energy becomes even greater when considering an electric car, since heavy use of the climate-control system may exhaust the battery. In this article we consider a method for an automated agent to provide drivers with advice that will motivate them to reduce the energy consumption of their climate-control unit. Our approach takes into account both the energy consumption of the climatecontrol system and the expected comfort level of the driver. We therefore have built two models, one for assessing the energy consumption of the climate-control system as a function of the system's settings, and the other for modeling the human comfort level as a function of the climate-control system's settings. Using these models, the agent provides advice to the driver considering how to set the climate-control system.