211724543
An Upper Bound on the Loss from Approximate Optimal-Value Functions
6543846
Predictive state representations with options
Recent work on predictive state representation (PSR) models has focused on using predictions of the outcomes of open-loop action sequences as state. These predictions answer questions of the form "What is the probability of seeing observation sequence o1, o2, ..., oN if the agent takes action sequence a1, a2, ..., aN from some given history?" We would like to ask more expressive questions in our representation of state, such as "If I behave according to some policy until I terminate, what will be my last observation?" We extend the linear PSR framework to answer questions like these about options -- temporally extended, closed-loop courses of action -- bounding the size of the linear PSR needed to model questions about a certain class of options. We introduce a hierarchical PSR (HPSR) that can make predictions about both options and primitive action sequences and show empirical results from learning HPSRs in simple domains.
2661414
Graphical Models for Game Theory
We introduce a compact graph-theoretic representation for multi-party game theory. Our main result is a provably correct and efficient algorithm for computing approximate Nash equilibria in one-stage games represented by trees or sparse graphs.
9771856
Nash Convergence of Gradient Dynamics in General-Sum Games
Multi-agent games are becoming an increasingly prevalent formalism for the study of electronic commerce and auctions. The speed at which transactions can take place and the growing complexity of electronic marketplaces makes the study of computationally simple agents an appealing direction. In this work, we analyze the behavior of agents that incrementally adapt their strategy through gradient ascent on expected payoff, in the simple setting of two-player, two-action, iterated general-sum games, and present a surprising result. We show that either the agents will converge to a Nash equilibrium, or if the strategies themselves do not converge, then their average payoffs will nevertheless converge to the payoffs of a Nash equilibrium.
2624439
Learning Predictive State Representations
We introduce the first algorithm for learning predictive state representations (PSRs), which are a way of representing the state of a controlled dynamical system. The state representation in a PSR is a vector of predictions of tests, where tests are sequences of actions and observations said to be true if and only if all the observations occur given that all the actions are taken. The problem of finding a good PSR. one that is a sufficient statistic for the dynamical system--can be divided into two parts: 1) discovery of a good set of tests, and 2) learning to make accurate predictions for those tests. In this paper, we present detailed empirical results using a gradient-based algorithm for addressing the second problem. Our results demonstrate several sample systems in which the algorithm learns to make correct predictions and several situations in which the algorithm is less successful. Our analysis reveals challenges that will need to be addressed in future PSR learning algorithms.
17854729
Reinforcement Learning Algorithms for Average-Payoff Markovian Decision Processes
Reinforcement learning (RL) has become a central paradigm for solving learning-control problems in robotics and artificial intelligence. RL researchers have focussed almost exclusively on problems where the controller has to maximize the discounted sum of payoffs. However, as emphasized by Schwartz (1993), in many problems, e.g., those for which the optimal behavior is a limit cycle, it is more natural and computationally advantageous to formulate tasks so that the controller's objective is to maximize the average payoff received per time step. In this paper I derive new average-payoff RL algorithms as stochastic approximation methods for solving the system of equations associated with the policy evaluation and optimal control questions in average-payoff RL tasks. These algorithms are analogous to the popular TD and Q-learning algorithms already developed for the discounted-payoff case. One of the algorithms derived here is a significant variation of Schwartz's R-learning algorithm. Preliminary empirical results are presented to validate these new algorithms.
33975335
UCD of financial services at the smart internet technology centre
In this paper I describe the experience of contributing sociological and anthropological perspectives to the user-centered design of financial services in the Smart Internet Technology Cooperative Research Centre in Australia.
14570975
DRMs, fair use and users' experience of sharing music
There is a mismatch between the law relating to fair use, personal use and copying; the central thrust of Digital Rights Management (DRM) and users' behavior relating to the listening and sharing of music. This paper reports on the different copyright regimes in the United States and Australia. It describes some of the current DRM systems. Against this background, the paper draws on a qualitative study to explore Australian users' experience of listening to and sharing music. A design for a good DRM has to take into account the schism between the copyright regimes and users' sharing behavior.
570980
An efficient on-line path planner for outdoor mobile robots
Abstract Mobile robots operating in outdoor unstructured environments often have only incomplete maps and must deal with new objects found during traversal. Path planning in these environments must be incremental to accommodate new information and must use efficient representations. This article reports recent results in path planning using an efficient data structure (framed-quadtrees) and an optimal algorithm (D*) to incrementally replan optimal paths. We show how the use of framed-quadtrees leads to paths that are shorter and more direct than when other representations are used. We also show the difference in performance when the robot starts with no information about the world versus when it starts with partial information about the world. Our results indicate that, as would be expected, starting with partial information is better than starting with no information. However, in many cases, partial information results in performance that is almost as good as starting out with complete information about the world, while the computational cost incurred is significantly lower. Our system has been tested in simulation as well on an autonomous jeep equipped with local obstacle avoidance capabilities. Results from both simulation and real experimentation are discussed.
748558
Automated Worm Fingerprinting
Network worms are a clear and growing threat to the security of today's Internet-connected hosts and networks. The combination of the Internet's unrestricted connectivity and widespread software homogeneity allows network pathogens to exploit tremendous parallelism in their propagation. In fact, modern worms can spread so quickly, and so widely, that no human-mediated reaction can hope to contain an outbreak. 
 
In this paper, we propose an automated approach for quickly detecting previously unknown worms and viruses based on two key behavioral characteristics - a common exploit sequence together with a range of unique sources generating infections and destinations being targeted. More importantly, our approach - called "content sifting" - automatically generates precise signatures that can then be used to filter or moderate the spread of the worm elsewhere in the network. 
 
Using a combination of existing and novel algorithms we have developed a scalable content sifting implementation with low memory and CPU requirements. Over months of active use at UCSD, our Earlybird prototype system has automatically detected and generated signatures for all pathogens known to be active on our network as well as for several new worms and viruses which were unknown at the time our system identified them. Our initial experience suggests that, for a wide range of network pathogens, it may be practical to construct fully automated defenses - even against so-called "zero-day" epidemics.
62491573
A Robotic Excavator for Autonomous Truck Loading
Excavators are used for the rapid removal of soil and other materials in mines, quarries, and construction sites. The automation of these machines offers promise for increasing productivity and improving safety. To date, most research in this area has focussed on selected parts of the problem. In this paper, we present a system that completely automates the truck loading task. The excavator uses two scanning laser rangefinders to recognize and localize the truck, measure the soil face, and detect obstacles. The excavator's software decides where to dig in the soil, where to dump in the truck, and how to quickly move between these points while detecting and stopping for obstacles. The system was fully implemented and was demonstrated to load trucks as fast as human operators.