Some issues in the design of market-oriented agents
T Mullen and MP Wellman
International Workshop on Agent Theories, Architectures, and Languages (ATAL-95), 1995.
Abstract
In a computational market, distributed market agents interact with other agents primarily through the exchange of…
The economic approach to artificial intelligence (position paper)
To take an economic approach to anything typically invokes three premises. First, that the fundamental problem to be solved is one of resource allocation. Second, that it is useful to model behavior in terms of a rationality abstraction. And third, that it is essential to consider how authority and activity may be decentralized. All three of these premises are being increasingly adopted (explicitly or implicitly) in artificial intelligence, and growing numbers of AI researchers are working within the economic paradigm.
Path Planning under Time-Dependent Uncertainty
Standard algorithms for finding the shortest path in a graph require that the cost of a path be additive in edge costs, and typically assume that costs are deterministic. We consider the problem of uncertain edge costs, with potential probabilistic dependencies among the costs. Although these dependencies violate the standard dynamic-programming decomposition, we identify a weaker stochastic consistency condition that justifies a generalized dynamic-programming approach based on stochastic dominance. We present a revised path-planning algorithm and prove that it produces optimal paths under time-dependent uncertain costs. We test the algorithm by applying it to a model of stochastic bus networks, and present empirical performance results comparing it to some alternatives. Finally, we consider extensions of these concepts to a more general class of problems of heuristic search under uncertainty.
Accounting for Context in Plan Recognition, with Application to Traffic Monitoring
Typical approaches to plan recognition start from a representation of an agent's possible plans, and reason evidentially from observations of the agent's actions to assess the plausibility of the various candidates. A more expansive view of the task (consistent with some prior work) accounts for the context in which the plan was generated, the mental state and planning process of the agent, and consequences of the agent's actions in the world. We present a general Bayesian framework encompassing this view, and focus on how context can be exploited in plan recognition. We demonstrate the approach on a problem in traffic monitoring, where the objective is to induce the plan of the driver from observation of vehicle movements. Starting from a model of how the driver generates plans, we show how the highway context can appropriately influence the recognizer's interpretation of observed driver behavior.
A computational market model for distributed configuration design
This paper presents a precise market model for a well-defined class of distributed configuration design problems. Given a design problem, the model defines a computational economy to allocate basic resources to agents participating in the design. The result of running these “design economies” constitutes the market solution to the original problem. After defining the configuration design framework, I describe the mapping to computational economies and our results to date. For some simple examples, the system can produce good designs relatively quickly. However, analysis shows that the design economies are not guaranteed to find optimal designs, and we identify and discuss some of the major pitfalls. Despite known shortcomings and limited explorations thus far, the market model offers a useful conceptual viewpoint for analyzing distributed design problems.
