Agent oriented programming: An overview of the framework and summary of recent research

This is a short overview of the agent-oriented programming (AOP) framework. AOP can be viewed as an specialization of object-oriented programming. The state of an agent consists of components called beliefs, choices, capabilities, commitments, and possibly others; for this reason the state of an agent is called its mental state. The mental state of agents is captured formally in an extension of standard epistemic logics: beside temporalizing the knowledge and belief operators, AOP introduces operators for commitment, choice and capability. Agents are controlled by agent programs, which include primitives for communicating with other agents. In the spirit of speech-act theory, each communication primitive is of a certain type: informing, requesting, offering, etc. This document describes these features in more detail and summarizes recent results and ongoing AOP-related work

Stable Invitations

We consider the situation in which an organizer is trying to convene an event, and needs to choose a subset of agents to be invited. Agents have preferences over how many attendees should be at the event and possibly also who the attendees should be. This induces a stability requirement: All invited agents should prefer attending to not attending, and all the other agents should not regret being not invited. The organizer's objective is to find the invitation of maximum size subject to the stability requirement. We investigate the computational complexity of finding the maximum stable invitation when all agents are truthful, as well as the mechanism design problem when agents may strategically misreport their preferences.Comment: To appear in COMSOC 201

An Overview of Combinatorial Auctions

An auction is combinatorial when bidders can place bids on combinations of items, called “packages,” rather than just individual items. Computer scientists are interested in combinatorial auctions because they are concerned with the expressiveness of bidding languages, as well as the algorithmic aspects of the underlying combinatorial problem. The combinatorial problem has attracted attention from operations researchers, especially those working in combinatorial optimization and mathematical programming, who are fascinated by the idea of applying these tools to auctions. Auctions have been studied extensively by economists, of course. Thus, the newly emerging field of combinatorial auctions lies at the intersection of computer science, operations research, and economics. In this article, we present a brief introduction to combinatorial auctions, based on our book, Combinatorial Auctions (MIT Press, 2006), in which we look at combinatorial auctions from all three perspectives.Auctions

Fair Imposition

We introduce a new mechanism-design problem called fair imposition. In this setting a center wishes to fairly allocate tasks among a set of agents whose cost structures are known only to them, and thus will not reveal their true costs without appropriate incentives. The center, with the power to impose arbitrary tasks and payments on the agents, has the additional goal that his net payment to these agents is never positive (or, that it is tightly bounded if a loss is unavoidable). We consider two di#erent notions of fairness that the center may wish to achieve. The central notion, which we call k-fairness, is in the spirit of max-min fairness. We present both positive results (in the form of concrete mechanisms) and negative results (in the form of impossibility theorems) concerning these criteria. We also briefly discuss an alternative, more traditional interpretation of our setting and results, in the context of auctions