Incremental planning to control a blackboard-based problem solver

To control problem solving activity, a planner must resolve uncertainty about which specific long-term goals (solutions) to pursue and about which sequences of actions will best achieve those goals. A planner is described that abstracts the problem solving state to recognize possible competing and compatible solutions and to roughly predict the importance and expense of developing these solutions. With this information, the planner plans sequences of problem solving activities that most efficiently resolve its uncertainty about which of the possible solutions to work toward. The planner only details actions for the near future because the results of these actions will influence how (and whether) a plan should be pursued. As problem solving proceeds, the planner adds new details to the plan incrementally, and monitors and repairs the plan to insure it achieves its goals whenever possible. Through experiments, researchers illustrate how these new mechanisms significantly improve problem solving decisions and reduce overall computation. They briefly discuss current research directions, including how these mechanisms can improve a problem solver's real-time response and can enhance cooperation in a distributed problem solving network

Ultracold neutral plasma expansion in two dimensions

We extend an isothermal thermal model of ultracold neutral plasma expansion to systems without spherical symmetry, and use this model to interpret new fluorescence measurements on these plasmas. By assuming a self-similar expansion, it is possible to solve the fluid equations analytically and to include velocity effects to predict the fluorescence signals. In spite of the simplicity of this approach, the model reproduces the major features of the experimental data

Notas sobre la filosofía y la Universidad

Fil: Davis, Harol E..Fil: Durfee, Harold A.

Security Attributes Based Digital Rights Management

Most real-life systems delegate responsibilities to different authorities. We apply this model to a digital rights management system, to achieve flexible security. In our model a hierarchy of authorities issues certificates that are linked by cryptographic means. This linkage establishes a chain of control, identity-attribute-rights, and allows flexible rights control over content. Typical security objectives, such as identification, authentication, authorization and access control can be realised. Content keys are personalised to detect illegal super distribution. We describe a working prototype, which we develop using standard techniques, such as standard certificates, XML and Java. We present experimental results to evaluate the scalability of the system. A formal analysis demonstrates that our design is able to detect a form of illegal super distribution

Resource Allocation Among Agents with MDP-Induced Preferences

Allocating scarce resources among agents to maximize global utility is, in general, computationally challenging. We focus on problems where resources enable agents to execute actions in stochastic environments, modeled as Markov decision processes (MDPs), such that the value of a resource bundle is defined as the expected value of the optimal MDP policy realizable given these resources. We present an algorithm that simultaneously solves the resource-allocation and the policy-optimization problems. This allows us to avoid explicitly representing utilities over exponentially many resource bundles, leading to drastic (often exponential) reductions in computational complexity. We then use this algorithm in the context of self-interested agents to design a combinatorial auction for allocating resources. We empirically demonstrate the effectiveness of our approach by showing that it can, in minutes, optimally solve problems for which a straightforward combinatorial resource-allocation technique would require the agents to enumerate up to 2^100 resource bundles and the auctioneer to solve an NP-complete problem with an input of that size

Fluorescence measurements of expanding strongly-coupled neutral plasmas

We report new detailed density profile measurements in expanding strongly-coupled neutral plasmas. Using laser-induced fluorescence techniques, we determine plasma densities in the range of 10^5 to 10^9/cm^3 with a time resolution limit as small as 7 ns. Strong-coupling in the plasma ions is inferred directly from the fluorescence signals. Evidence for strong-coupling at late times is presented, confirming a recent theoretical result.Comment: submitted to PR

Use of Markov Chains to Design an Agent Bidding Strategy for Continuous Double Auctions

As computational agents are developed for increasingly complicated e-commerce applications, the complexity of the decisions they face demands advances in artificial intelligence techniques. For example, an agent representing a seller in an auction should try to maximize the seller's profit by reasoning about a variety of possibly uncertain pieces of information, such as the maximum prices various buyers might be willing to pay, the possible prices being offered by competing sellers, the rules by which the auction operates, the dynamic arrival and matching of offers to buy and sell, and so on. A naive application of multiagent reasoning techniques would require the seller's agent to explicitly model all of the other agents through an extended time horizon, rendering the problem intractable for many realistically-sized problems. We have instead devised a new strategy that an agent can use to determine its bid price based on a more tractable Markov chain model of the auction process. We have experimentally identified the conditions under which our new strategy works well, as well as how well it works in comparison to the optimal performance the agent could have achieved had it known the future. Our results show that our new strategy in general performs well, outperforming other tractable heuristic strategies in a majority of experiments, and is particularly effective in a 'seller?s market', where many buy offers are available