If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
An, Bo (University of Southern California) | Shieh, Eric (University of Southern California) | Tambe, Milind (University of Southern California) | Yang, Rong (University of Southern California) | Baldwin, Craig (United States Coast Guard) | DiRenzo, Joseph (United States Coast Guard) | Maule, Ben (United States Coast Guard) | Meyer, Garrett (United States Coast Guard)
While three deployed applications of game theory for security have recently been reported, we as a community of agents and AI researchers remain in the early stages of these deployments; there is a continuing need to understand the core principles for innovative security applications of game theory. PROTECT is premised on an attacker-defender Stackelberg game model and offers five key innovations. First, this system is a departure from the assumption of perfect adversary rationality noted in previous work, relying instead on a quantal response (QR) model of the adversary's behavior --- to the best of our knowledge, this is the first real-world deployment of the QR model. Fourth, our experimental results illustrate that PROTECT's QR model more robustly handles real-world uncertainties than a perfect rationality model.
Yin, Zhengyu (University of Southern California) | Jiang, Albert Xin (University of Southern California) | Tambe, Milind (University of Southern California) | Kiekintveld, Christopher (University of Texas at El Paso) | Leyton-Brown, Kevin (University of British Columbia) | Sandholm, Tuomas (Carnegie Mellon University) | Sullivan, John P. (Los Angeles County Sheriff's Department)
In proof-of-payment transit systems, passengers are legally required to purchase tickets before entering but are not physically forced to do so. Instead, patrol units move about the transit system, inspecting the tickets of passengers, who face fines if caught fare evading. TRUSTS models the problem of computing patrol strategies as a leader-follower Stackelberg game where the objective is to deter fare evasion and hence maximize revenue. We present an efficient algorithm for computing such patrol strategies and present experimental results using real-world ridership data from the Los Angeles Metro Rail system.
Alani, Harith (The Open University) | An, Bo (University of Southern California) | Jain, Manish (University of Southern California) | Kido, Takashi (Rikengenesis) | Konidaris, George (Massachusetts Institute of Technology) | Lawless, William (Paine College) | Martin, David (Apple Computer) | Pantofaru, Caroline (Willow Garage, Inc.) | Sofge, Donald (Naval Research Laboratory) | Takadama, Keiki (University of Electro-Communications) | Tambe, Milind (University of Southern California) | Vitvar, Tomas (Czech Technical University)
The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University's Department of Computer Science, was pleased to present the 2012 Spring Symposium Series, held Monday through Wednesday, March 26–28, 2012 at Stanford University, Stanford, California USA. The six symposia held were AI, The Fundamental Social Aggregation Challenge (cochaired by W. F. Lawless, Don Sofge, Mark Klein, and Laurent Chaudron); Designing Intelligent Robots (cochaired by George Konidaris, Byron Boots, Stephen Hart, Todd Hester, Sarah Osentoski, and David Wingate); Game Theory for Security, Sustainability, and Health (cochaired by Bo An and Manish Jain); Intelligent Web Services Meet Social Computing (cochaired by Tomas Vitvar, Harith Alani, and David Martin); Self-Tracking and Collective Intelligence for Personal Wellness (cochaired by Takashi Kido and Keiki Takadama); and Wisdom of the Crowd (cochaired by Caroline Pantofaru, Sonia Chernova, and Alex Sorokin). The papers of the six symposia were published in the AAAI technical report series.
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.
The operation of a human organization requires dozens of everyday tasks to ensure coherence in organizational activities, monitor the status of such activities, gather information relevant to the organization, keep everyone in the organization informed, and so on. Based on this vision, this article reports on ELECTRIC ELVES, a system that has been operational 24 hours a day, 7 days a week at our research institute since 1 June 2000. Tied to individual user workstations, fax machines, voice, and mobile devices such as cell phones and palm pilots, ELECTRIC ELVES has assisted us in routine tasks, such as rescheduling meetings, selecting presenters for research meetings, tracking people's locations, organizing lunch meetings, and so on. We also report the results of deploying ELECTRIC ELVES in our own research organization.
With the growing importance of multiagent team-work, tools that can help humans analyze, evaluate, and understand team behaviors are also becoming increasingly important. ISAAC'S novelty stems from a key design constraint that arises in team analysis: Multiple types of models of team behavior are necessary to analyze different granularities of team events, including agent actions, interactions, and global performance. Additionally, ISAAC uses multiple presentation techniques that can aid human understanding of the analyses. This article presents ISAAC'S general conceptual framework and its application in the RoboCup soccer domain, where ISAAC was awarded the RoboCup Scientific Challenge Award.
The Robot World Cup Soccer Games and Conferences (RoboCup) are a series of competitions and events designed to promote the full integration of AI and robotics research. Following the first RoboCup, held in Nagoya, Japan, in 1997, RoboCup-98 was held in Paris from 2-9 July, overlapping with the real World Cup soccer competition. RoboCup-98 included competitions in three leagues: (1) the simulation league, (2) the real robot small-size league, and (3) the real robot middle-size league. Champion teams were cmunited-98 in both the simulation and the real robot small-size leagues and cs-freiburg (Freiburg, Germany) in the real robot middle-size league.
Tambe, Milind, Jung, Hyuckchul
In a complex, dynamic multiagent setting, coherent team actions are often jeopardized by conflicts in agents' beliefs, plans, and actions. CONSA focuses on exploiting the benefits of argumentation in a team setting. Thus, CONSA casts conflict resolution as a team problem, so that the recent advances in teamwork can be brought to bear during conflict resolution to improve argumentation flexibility. Furthermore, because teamwork conflicts sometimes involve past teamwork, teamwork models can be exploited to provide agents with reusable argumentation knowledge.