7307228
Predictive Representations of State
We show that states of a dynamical system can be usefully represented by multi-step, action-conditional predictions of future observations. State representations that are grounded in data in this way may be easier to learn, generalize better, and be less dependent on accurate prior models than, for example, POMDP state representations. Building on prior work by Jaeger and by Rivest and Schapire, in this paper we compare and contrast a linear specialization of the predictive approach with the state representations used in POMDPs and in k-order Markov models. Ours is the first specific formulation of the predictive idea that includes both stochasticity and actions (controls). We show that any system has a linear predictive state representation with number of predictions no greater than the number of states in its minimal POMDP model.
5053575
Bias-Variance Error Bounds for Temporal Difference Updates
Temporal difference (td) algorithms are used in reinforcement learning to compute estimates of the value of a given policy in an unknown Markov decision process (policy evaluation). We give rigorous upper bounds on the error of the closely related phased td algorithms (which differ from the standard updates in their treatment of the learning rate) as a function of the amount of experience. These upper bounds prove exponentially fast convergence, with both the rate of convergence and the asymptote strongly dependent on the length of the backups k or the parameter . Our bounds give formal verification to the well-known intuition that td methods are subject to a bias-variance tradeoff, and they lead to schedules for k and that are predicted to be better than any fixed values for these parameters. We give preliminary experimental confirmation of our theory for a version of the random walk problem.
7241207
Intrinsically Motivated Reinforcement Learning
Psychologists call behavior intrinsically motivated when it is engaged in for its own sake rather than as a step toward solving a specific problem of clear practical value. But what we learn during intrinsically motivated behavior is essential for our development as competent autonomous entities able to efficiently solve a wide range of practical problems as they arise. In this paper we present initial results from a computational study of intrinsically motivated reinforcement learning aimed at allowing artificial agents to construct and extend hierarchies of reusable skills that are needed for competent autonomy.
2695116
Near-Optimal Reinforcement Learning in Polynomial Time
We present new algorithms for reinforcement learning and prove that they have polynomial bounds on the resources required to achieve near-optimal return in general Markov decision processes. After observing that the number of actions required to approach the optimal return is lower bounded by the mixing time T of the optimal policy (in the undiscounted case) or by the horizon time T (in the discounted case), we then give algorithms requiring a number of actions and total computation time that are only polynomial in T and the number of states and actions, for both the undiscounted and discounted cases. An interesting aspect of our algorithms is their explicit handling of the Exploration-Exploitation trade-off.
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.
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.
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.
14183054
Framed-quadtree path planning for mobile robots operating in sparse environments
Mobile robots operating in vast outdoor unstructured environments often only have incomplete maps and must deal with new objects found during traversal. Path planning in such sparsely occupied regions must be incremental to accommodate new information, and, must use efficient representations. In previous work we have developed an optimal method D* to plan paths when the environment is not known ahead of time, but, rather is discovered as the robot moves around. To date, D* has been applied to a uniform grid representation for obstacles and free space. In this paper we propose the use of D* with framed quadtrees to improve the efficiency of planning paths in sparse environments. The new system has been tested in simulation as well on an autonomous jeep, equipped with local obstacle avoidance capabilities. We show how the use of framed quadtrees improves performance in terms of path length, computation speed, and memory requirements.
1823400
Models for Automated Earthmoving
We present a composite forward model of the mechanics of an excavator backhoe digging in soil. This model is used to predict the trajectories developed by a closed-loop force based control scheme given initial conditions, some of which can be controlled (excavator control parameters), some directly measured (shape of the terrain), and some estimated (soil properties). Since soil conditions can vary significantly, it is necessary that soil properties be estimated online. Our models are used to both estimate soil properties and predict contact forces between the excavator and the terrain. In a large set of experiments we have conducted, we find that these models are accurate to within approximately 20% and run about 10 times faster than real-time. In this paper we motivate the development of these models and discuss experimental data from our testbed.
18021791
Energy Consumption of TCP in Ad Hoc Networks
In this paper we study the energy cost (protocol processing and communication cost) and goodput of different flavors of TCP (Transmission Control Protocol) in ad hoc networks. We implemented a testbed and measured the actual energy cost as well as goodput of running TCP Reno, Newreno, SACK (Selective ACKnowledgement) and a version that combines Explicit Link Failure Notification (ELFN) [7] and Explicit Congestion Notification (ECN) [5] in Newreno. We see that the use of ECN & ELFN does yield higher goodput in most cases with a corresponding lower total energy cost. We see an energy savings of between 20% and 500% depending on the network conditions.
1850582
On Scalable Attack Detection in the Network
Current intrusion detection and prevention systems seek to detect a wide class of network intrusions (e.g., DoS attacks, worms, port scans) at network vantage points. Unfortunately, even today, many IDS systems we know of keep per-connection or per-flow state to detect malicious TCP flows. Thus, it is hardly surprising that these IDS systems have not scaled to multi-gigabit speeds. By contrast, both router lookups and fair queuing have scaled to high speeds using aggregation via prefix lookups or DiffServ. Thus, in this paper, we initiate research into the question as to whether one can detect attacks without keeping per-flow state. We will show that such aggregation, while making fast implementations possible, immediately causes two problems. First, aggregation can cause behavioral aliasing where, for example, good behaviors can aggregate to look like bad behaviors. Second, aggregated schemes are susceptible to spoofing by which the intruder sends attacks that have appropriate aggregate behavior. We examine a wide variety of DoS and scanning attacks and show that several categories (bandwidth based, claim-and-hold, port-scanning) can be scalably detected. In addition to existing approaches for scalable attack detection, we propose a novel data structure called partial completion filters (PCFs) that can detect claim-and-hold attacks scalably in the network. We analyze PCFs both analytically and using experiments on real network traces to demonstrate how we can tune PCFs to achieve extremely low false positive and false negative probabilities