Author name cluster
José M. Vidal
Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.
Possible papers
7JAAMAS Journal 2026 Journal Article
Predicting the Expected Behavior of Agents that Learn About Agents: The CLRI Framework
- José M. Vidal
- Edmund H. Durfee
Abstract We describe a framework and equations used to model and predict the behavior of multi-agent systems (MASs) with learning agents. A difference equation is used for calculating the progression of an agent's error in its decision function, thereby telling us how the agent is expected to fare in the MAS. The equation relies on parameters which capture the agent's learning abilities, such as its change rate, learning rate and retention rate, as well as relevant aspects of the MAS such as the impact that agents have on each other. We validate the framework with experimental results using reinforcement learning agents in a market system, as well as with other experimental results gathered from the AI literature. Finally, we use PAC-theory to show how to calculate bounds on the values of the learning parameters.
AAMAS Conference 2007 Conference Paper
Bidding Algorithms for a Distributed Combinatorial Auction
- Benito Mendoza
- José M. Vidal
Distributed allocation and multiagent coordination problems can be solved through combinatorial auctions. However, most of the existing winner determination algorithms for combinatorial auctions are centralized. The PAUSE auction is one of a few efforts to release the auctioneer from having to do all the work (it might even be possible to get rid of the auctioneer). It is an increasing price combinatorial auction that naturally distributes the problem of winner determination amongst the bidders in such a way that they have an incentive to perform the calculation. It can be used when we wish to distribute the computational load among the bidders or when the bidders do not wish to reveal their true valuations unless necessary. PAUSE establishes the rules the bidders must obey. However, it does not tell us how the bidders should calculate their bids. We have developed a couple of bidding algorithms for the bidders in a PAUSE auction. Our algorithms always return the set of bids that maximizes the bidder's utility. Since the problem is NP-Hard, run time remains exponential on the number of items, but it is remarkably better than an exhaustive search. In this paper we present our bidding algorithms, discuss their virtues and drawbacks, and compare the solutions obtained by them to the revenue-maximizing solution found by a centralized winner determination algorithm.
AAAI Conference 1996 Short Paper
Agents Modeling Agents in Information Economies
- José M. Vidal
Our goal is to design and build agents that act intelligently when placed in an agent-based information economy, where agents buy and sell services (e.g. thesaurus, search, task planning services, etc.). The economy we are working in is the University of Michigan Digital Library (UMDL), a large scale multidisciplinary effort to build an infrastructure for the delivery of library services [2]. In contrast with a typical economy, an information economy deals in goods and services that are often derived from unique sources (authors, analysts, etc.), so that many goods and services are not interchangeable. Also, the cost of replicating and transporting goods is usually negligible, and the quality of goods and services is difficult to measure objectively: even two sources with essentially the same information might appeal to different audiences. Thus, each agent has its own assessment of the quality of goods and services delivered.
AAAI Conference 1994 Short Paper
Agent Modeling Methods Using Limited Rationality
- José M. Vidal
To decide what to do in a multiagent world, an agent should model what others might simultaneously be deciding to do, but that in turn requires modeling what those others might think that others are deciding to do, and so on. The Recursive Modeling Method (RMM) [I] provides representations and algorithms for developing these nested models of beliefs and using them to make rational choices of action. However, because these nested models can involve many branches and recurse deeply, making decisions in time-constrained multiagent worlds requires methods for inexpensive approximation and for metareasoning to balance decision quality with decisionmaking cost.