? This thesis provides a clarification of trust. We present a formalism for trust which provides us with a tool for precise discussion. The formalism is implementable: it can be embedded in an artificial agent, enabling the agent to make trust-based decisions. Its applicability in the domain of Distributed

A network of people connected by directed ratings or trust scores, and a model for propagating those trust scores, is a fundamental building block in many of today's most successful e-commerce and recommendation systems. In eBay, such a model of trust has significant influence on the price

characteristics, and based on a reputation mechanism, or word-of-mouth. Our proposed model allows agents to decide which other agents' opinions they trust more and allows agents to progressively tune their understanding of another agent's subjective recommendations.

Peer-to-peer (P2P) online communities are commonly perceived as an environment offering both opportunities and threats. One way to

into account the auto-correlation of the data) works well when the number of states is large enough. Two corrections based on asymptotic approximation of the variance-covariance matrix work well for moderate numbers of states and one correction that collapses the time series information into a “pre ” and “post

We propose a new approach to reinforcement learning for control problems which combines value-function approximation with linear architectures and approximate policy iteration. This new approach

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