Rational bidding using reinforcement learning: an application in automated resource allocation

The application of autonomous agents by the provisioning and usage of computational resources is an attractive research field. Various methods and technologies in the area of artificial intelligence, statistics and economics are playing together to achieve i) autonomic resource provisioning and usage of computational resources, to invent ii) competitive bidding strategies for widely used market mechanisms and to iii) incentivize consumers and providers to use such market-based systems. The contributions of the paper are threefold. First, we present a framework for supporting consumers and providers in technical and economic preference elicitation and the generation of bids. Secondly, we introduce a consumer-side reinforcement learning bidding strategy which enables rational behavior by the generation and selection of bids. Thirdly, we evaluate and compare this bidding strategy against a truth-telling bidding strategy for two kinds of market mechanisms – one centralized and one decentralized

Q-Strategy: A Bidding Strategy for Market-Based Allocation of Grid Services

The application of autonomous agents by the provisioning and usage of computational services is an attractive research field. Various methods and technologies in the area of artificial intelligence, statistics and economics are playing together to achieve i) autonomic service provisioning and usage of Grid services, to invent ii) competitive bidding strategies for widely used market mechanisms and to iii) incentivize consumers and providers to use such market-based systems. The contributions of the paper are threefold. First, we present a bidding agent framework for implementing artificial bidding agents, supporting consumers and providers in technical and economic preference elicitation as well as automated bid generation by the requesting and provisioning of Grid services. Secondly, we introduce a novel consumer-side bidding strategy, which enables a goal-oriented and strategic behavior by the generation and submission of consumer service requests and selection of provider offers. Thirdly, we evaluate and compare the Q-strategy, implemented within the presented framework, against the Truth-Telling bidding strategy in three mechanisms – a centralized CDA, a decentralized on-line machine scheduling and a FIFO-scheduling mechanisms

An Investigation of Different Modeling Techniques for Autonomous Robot Navigation

This research aims to give recommendations towards modeling the navigation control architectures for an autonomous rover designed for an unstructured, outdoors environment. These recommendations are equally applicable to other autonomous vehicles, such as aircraft or underwater vehicles. Many successful architectures for this application have been developed, but there is no common terminology for the discussion of robotics architectures and their properties in general. This paper suggests the use of terms borrowed from administrative theory to facilitate interdisciplinary dialog about the tradeoffs of various kinds of models for robotics and similar systems. Past approaches to modeling autonomous robot navigation architectures have broken the architecture up into layers or levels. The upper levels or layers make high-level decisions about how the robot is going to accomplish a task, and the lower levels or layers make low-level decisions. This is analogous to a CEO of a corporation telling the managers how he wants the corporation to work towards its goal. The managers each oversee a part of the corporation. The workers are told what to do, but still make low-level decisions such as how hard to twist a screw, what tool to use to remove a rivet, or to do something other than what they were told in the interest of safety. Traditionally, there have been two or three layers for robot architectures, and every module developed fits into one of these layers. Every branch of the hierarchy has one module in each of the layers. The reasons given for breaking the architecture up into two or three layers vary from implementation to implementation. This paper aims to take a more generalized view. The benefits of the two or three layered approach are well published, including reliability, reusability, and scalability among others. This paper asserts that these layers are unnecessary, and that vertical specialization can be implemented to a different degree on different branches of the hierarchy. For example, the velocity controller on a rover might have two layers, whereas the steering controller on the same rover might have four. They share the highest layer, which is the navigational planner that coordinates them. But the two branches of hierarchy between the navigational planner and the two actuators look very different from one another. This facilitates a decentralization of the decision making duties and greater freedom in the process of breaking the navigation system up into modules

Robot Mindreading and the Problem of Trust

This paper raises three questions regarding the attribution of beliefs, desires, and intentions to robots. The first one is whether humans in fact engage in robot mindreading. If they do, this raises a second question: does robot mindreading foster trust towards robots? Both of these questions are empirical, and I show that the available evidence is insufficient to answer them. Now, if we assume that the answer to both questions is affirmative, a third and more important question arises: should developers and engineers promote robot mindreading in view of their stated goal of enhancing transparency? My worry here is that by attempting to make robots more mind-readable, they are abandoning the project of understanding automatic decision processes. Features that enhance mind-readability are prone to make the factors that determine automatic decisions even more opaque than they already are. And current strategies to eliminate opacity do not enhance mind-readability. The last part of the paper discusses different ways to analyze this apparent trade-off and suggests that a possible solution must adopt tolerable degrees of opacity that depend on pragmatic factors connected to the level of trust required for the intended uses of the robot

Demo: Making Plans Scrutable with Argumentation and Natural Language Generation.

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SAsSy - Making Decisions Transparent with Argumentation and Natural Language Generation

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