2178559
Efficient join processing over uncertain data
In many applications data values are inherently uncertain. This includes moving-objects, sensors and biological databases. There has been recent interest in the development of database management systems that can handle uncertain data. Some proposals for such systems include attribute values that are uncertain. In particular, an attribute value can be modeled as a range of possible values, associated with a probability density function. Previous efforts for this type of data have only addressed simple queries such as range and nearest-neighbor queries. Queries that join multiple relations have not been addressed in earlier work despite the significance of joins in databases. In this paper we address join queries over uncertain data. We propose a semantics for the join operation, define probabilistic operators over uncertain data, and propose join algorithms that provide efficient execution of probabilistic joins. The paper focuses on an important class of joins termed probabilistic threshold joins that avoid some of the semantic complexities of dealing with uncertain data. For this class of joins we develop three sets of optimization techniques: item-level, page-level, and index-level pruning. These techniques facilitate pruning with little space and time overhead, and are easily adapted to most join algorithms. We verify the performance of these techniques experimentally.
3396566
U-DBMS: A Database System for Managing Constantly-Evolving Data
In many systems, sensors are used to acquire information from external environments such as temperature, pressure and locations. Due to continuous changes in these values, and limited resources (e.g., network bandwidth and battery power), it is often infeasible for the database to store the exact values at all times. Queries that uses these old values can produce invalid results. In order to manage the uncertainty between the actual sensor value and the database value, we propose a system called U-DBMS. U-DBMS extends the database system with uncertainty management functionalities. In particular, each data value is represented as an interval and a probability distribution function, and it can be processed with probabilistic query operators to produce imprecise (but correct) answers. This demonstration presents a PostgreSQL-based system that handles uncertainty and probabilistic queries for constantly-evolving data.
5600863
Learning Without State-Estimation in Partially Observable Markovian Decision Processes
Abstract Reinforcement learning (RL) algorithms provide a sound theoretical basis for building learning control architectures for embedded agents. Unfortunately all of the theory and much of the practice (see Barto et al ., 1983, for an exception) of RL is limited to Markovian decision processes (MDPs). Many real-world decision tasks, however, are inherently non-Markovian, i.e., the state of the environment is only incompletely known to the learning agent. In this paper we consider only partially observable MDPs (POMDPs), a useful class of non-Markovian decision processes. Most previous approaches to such problems have combined computationally expensive state-estimation techniques with learning control. This paper investigates learning in POMDPs without resorting to any form of state estimation. We present results about what TD(0) and Q-learning will do when applied to POMDPs. It is shown that the conventional discounted RL framework is inadequate to deal with POMDPs. Finally we develop a new framework for learning without state-estimation in POMDPs by including stochastic policies in the search space, and by defining the value or utility of a distribution over states.
2391025
Preliminary results in range-only localization and mapping
This paper presents methods of localization using cooperating landmarks (beacons) that provide the ability to measure range only. Recent advances in radio frequency technology make it possible to measure range between inexpensive beacons and a transponder Such a method has tremendous benefit since line of sight is not required between the beacons and the transponder and because the data association problem can be completely avoided. If the positions of the beacons are known, measurements from multiple beacons can be combined using probability grids to provide an accurate estimate of robot location. This estimate can be improved by using Monte Carlo techniques and Kalman filters to incorporate odometry data. Similar methods can be used to solve the simultaneous localization and mapping problem (SLAM) when beacon locations are uncertain. Experimental results are presented for robot localization. Tracking and SLAM algorithms are demonstrated in simulation.
2025191
Cobot in LambdaMOO: A Social Statistics Agent
We describe our development of Cobot, a software agent who lives in LambdaMOO, a popular virtual world frequented by hundreds of users. We present a detailed discussion of the functionality that has made him one of the objects most frequently interacted with in LambdaMOO, human or artificial.
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.
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.