Analysing imperfect temporal information in GIS using the Triangular Model

Rough set and fuzzy set are two frequently used approaches for modelling and reasoning about imperfect time intervals. In this paper, we focus on imperfect time intervals that can be modelled by rough sets and use an innovative graphic model [i.e. the triangular model (TM)] to represent this kind of imperfect time intervals. This work shows that TM is potentially advantageous in visualizing and querying imperfect time intervals, and its analytical power can be better exploited when it is implemented in a computer application with graphical user interfaces and interactive functions. Moreover, a probabilistic framework is proposed to handle the uncertainty issues in temporal queries. We use a case study to illustrate how the unique insights gained by TM can assist a geographical information system for exploratory spatio-temporal analysis

Auctions with imperfect commitment when the reserve may signal the auctioneer's type

If bidders are uncertain whether the auctioneer sticks to the announced reserve, some bidders respond by not bidding, speculating that the auctioneer may revoke the reserve. However, the reserve inadvertently signals the auctioneer's type, which drives a unique separating and a multitude of pooling equilibria. If one eliminates belief systems that violate the "intuitive criterion", one obtains a unique equilibrium reserve price equal to the seller's own valuation. Paradoxically, even if bidders initially believe that the auctioneer is bound by his reserve almost with certainty, commitment has no value

Formal foundations for semantic theories of nominalisation

This paper develops the formal foundations of semantic theories dealing with various kinds of nominalisations. It introduces a combination of an event-calculus with a type-free theory which allows a compositional description to be given of such phenomena like Vendler's distinction between perfect and imperfect nominals, iteration of gerunds and Cresswell's notorious non-urrival of'the train examples. Moreover, the approach argued for in this paper allows a semantic explanation to be given for a wide range of grammatical observations such as the behaviour of certain tpes of nominals with respect to their verbal contexts or the distribution of negation in nominals

Error matrices in quantum process tomography

We discuss characterization of experimental quantum gates by the error matrix, which is similar to the standard process matrix $\chi$ in the Pauli basis, except the desired unitary operation is factored out, by formally placing it either before or after the error process. The error matrix has only one large element, which is equal to the process fidelity, while other elements are small and indicate imperfections. The imaginary parts of the elements along the left column and/or top row directly indicate the unitary imperfection and can be used to find the needed correction. We discuss a relatively simple way to calculate the error matrix for a composition of quantum gates. Similarly, it is rather straightforward to find the first-order contribution to the error matrix due to the Lindblad-form decoherence. We also discuss a way to identify and subtract the tomography procedure errors due to imperfect state preparation and measurement. In appendices we consider several simple examples of the process tomography and also discuss an intuitive physical interpretation of the Lindblad-form decoherence.Comment: 21 pages (slightly revised version

How difficult it is to prove the quantumness of macroscropic states?

General wisdom tells us that if two quantum states are ``macroscopically distinguishable'' then their superposition should be hard to observe. We make this intuition precise and general by quantifying the difficulty to observe the quantum nature of a superposition of two states that can be distinguished without microscopic accuracy. First, we quantify the distinguishability of any given pair of quantum states with measurement devices lacking microscopic accuracy, i.e. measurements suffering from limited resolution or limited sensitivity. Next, we quantify the required stability that have to be fulfilled by any measurement setup able to distinguish their superposition from a mere mixture. Finally, by establishing a relationship between the stability requirement and the ``macroscopic distinguishability'' of the two superposed states, we demonstrate that indeed, the more distinguishable the states are, the more demanding are the stability requirements.Comment: 6 pages, 2 figure

Differentiable Algorithm Networks for Composable Robot Learning

This paper introduces the Differentiable Algorithm Network (DAN), a composable architecture for robot learning systems. A DAN is composed of neural network modules, each encoding a differentiable robot algorithm and an associated model; and it is trained end-to-end from data. DAN combines the strengths of model-driven modular system design and data-driven end-to-end learning. The algorithms and models act as structural assumptions to reduce the data requirements for learning; end-to-end learning allows the modules to adapt to one another and compensate for imperfect models and algorithms, in order to achieve the best overall system performance. We illustrate the DAN methodology through a case study on a simulated robot system, which learns to navigate in complex 3-D environments with only local visual observations and an image of a partially correct 2-D floor map.Comment: RSS 2019 camera ready. Video is available at https://youtu.be/4jcYlTSJF4

Adaptive Regret Minimization in Bounded-Memory Games

Online learning algorithms that minimize regret provide strong guarantees in situations that involve repeatedly making decisions in an uncertain environment, e.g. a driver deciding what route to drive to work every day. While regret minimization has been extensively studied in repeated games, we study regret minimization for a richer class of games called bounded memory games. In each round of a two-player bounded memory-m game, both players simultaneously play an action, observe an outcome and receive a reward. The reward may depend on the last m outcomes as well as the actions of the players in the current round. The standard notion of regret for repeated games is no longer suitable because actions and rewards can depend on the history of play. To account for this generality, we introduce the notion of k-adaptive regret, which compares the reward obtained by playing actions prescribed by the algorithm against a hypothetical k-adaptive adversary with the reward obtained by the best expert in hindsight against the same adversary. Roughly, a hypothetical k-adaptive adversary adapts her strategy to the defender's actions exactly as the real adversary would within each window of k rounds. Our definition is parametrized by a set of experts, which can include both fixed and adaptive defender strategies. We investigate the inherent complexity of and design algorithms for adaptive regret minimization in bounded memory games of perfect and imperfect information. We prove a hardness result showing that, with imperfect information, any k-adaptive regret minimizing algorithm (with fixed strategies as experts) must be inefficient unless NP=RP even when playing against an oblivious adversary. In contrast, for bounded memory games of perfect and imperfect information we present approximate 0-adaptive regret minimization algorithms against an oblivious adversary running in time n^{O(1)}.Comment: Full Version. GameSec 2013 (Invited Paper

Remembering as a mental action

Many philosophers consider that memory is just a passive information retention and retrieval capacity. Some information and experiences are encoded, stored, and subsequently retrieved in a passive way, without any control or intervention on the subject’s part. In this paper, we will defend an active account of memory according to which remembering is a mental action and not merely a passive mental event. According to the reconstructive account, memory is an imaginative reconstruction of past experience. A key feature of the reconstructive account is that given the imperfect character of memory outputs, some kind of control is needed. Metacognition is the control of mental processes and dispositions. Drawing from recent work on the normativity of automaticity and automatic control, we distinguish two kinds of metacognitive control: top-down, reflective control, on the one hand, and automatic, intuitive, feeling-based control on the other. Thus, we propose that whenever the mental process of remembering is controlled by means of intuitive or feeling-based metacognitive processes, it is an action