209 research outputs found
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
Expected Utility Networks
We introduce a new class of graphical representations, expected utility
networks (EUNs), and discuss some of its properties and potential applications
to artificial intelligence and economic theory. In EUNs not only probabilities,
but also utilities enjoy a modular representation. EUNs are undirected graphs
with two types of arc, representing probability and utility dependencies
respectively. The representation of utilities is based on a novel notion of
conditional utility independence, which we introduce and discuss in the context
of other existing proposals. Just as probabilistic inference involves the
computation of conditional probabilities, strategic inference involves the
computation of conditional expected utilities for alternative plans of action.
We define a new notion of conditional expected utility (EU) independence, and
show that in EUNs node separation with respect to the probability and utility
subgraphs implies conditional EU independence.Comment: Appears in Proceedings of the Fifteenth Conference on Uncertainty in
Artificial Intelligence (UAI1999
An Overview of Combinatorial Auctions
An auction is combinatorial when bidders can place bids on combinations of items, called āpackages,ā 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
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Temporal Notation and Casual Terminology
We argue that causal reasoning is an essential part of intelligent human behavior, and that discussion of it cannot be divorced from discussion of temporal reasoning. We therefore set out to define causation in three stages. In the first, we present an ontology of time. We then outline a theory of "causal conditional", which allows one to reason about multiple possible courses of events. Finally, we define causation in terms of direct e=causation and causal origin
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