47063936
Predictive Linear-Gaussian Models of Stochastic Dynamical Systems
Models of dynamical systems based on predictive state representations (PSRs) are defined strictly in terms of observable quantities, in contrast with traditional models (such as Hidden Markov Models) that use latent variables or state-space representations. In addition, PSRs have an effectively infinite memory, allowing them to model some systems that finite memory-based models cannot. Thus far, PSR models have primarily been developed for domains with discrete observations. Here, we develop the Predictive Linear-Gaussian (PLG) model, a class of PSR models for domains with continuous observations. We show that PLG models subsume Linear Dynamical System models (also called Kalman filter models or state-space models) while using fewer parameters. We also introduce an algorithm to estimate PLG parameters from data, and contrast it with standard Expectation Maximization (EM) algorithms used to estimate Kalman filter parameters. We show that our algorithm is a consistent estimation procedure and present preliminary empirical results suggesting that our algorithm outperforms EM, particularly as the model dimension increases.
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.
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.
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.
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.
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.
668272
First Results in the Coordination of Heterogeneous Robots for Large-Scale Assembly
While many multi-robot systems rely on fortuitous cooperation between agents, some tasks, such as the assembly of large structures, require tighter coordination. We present a general software architecture for coordinating heterogeneous robots that allows for both autonomy of the individual agents as well as explicit coordination. This paper presents recent results with three robots with very different configurations. Working as a team, these robots are able to perform a high-precision docking task that none could achieve individually.
32064388
Password sharing: implications for security design based on social practice
Current systems for banking authentication require that customers not reveal their access codes, even to members of the family. A study of banking and security in Australia shows that the practice of sharing passwords does not conform to this requirement. For married and de facto couples, password sharing is seen as a practical way of managing money and a demonstration of trust. Sharing Personal Identification Numbers (PINs) is a common practice among remote indigenous communities in Australia. In areas with poor banking access, this is the only way to access cash. People with certain disabilities have to share passwords with carers, and PIN numbers with retail clerks. In this paper we present the findings of a qualitative user study of banking and money management. We suggest design criteria for banking security systems, based on observed social and cultural practices of password and PIN number sharing.
17852912
Lock Free Data Structures Using STM in Haskell
This paper explores the feasibility of re-expressing concurrent algorithms with explicit locks in terms of lock free code written using Haskell's implementation of software transactional memory. Experimental results are presented which show that for multi-processor systems the simpler lock free implementations offer superior performance when compared to their corresponding lock based implementations.
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.