Rules of Encounter: Designing Conventions for Automated Negotiation Among Computers
Rules of Encounter applies the general approach and the mathematical tools of game theory in a formal analysis of rules (or protocols) governing the high-level behavior of interacting heterogeneous computer systems. It describes a theory of high-level protocol design that can be used to constrain manipulation and harness the potential of automated negotiation and coordination strategies to attain more effective interaction among machines that have been programmed by different entities to pursue different goals.
While game theoretic ideas have been used to answer the question of how a computer should be programmed to act in a given specific interaction, here they are used in a new way, to address the question of how to design the rules of interaction themselves for automated agents.
Rules of Encounter provides a unified, coherent account of machine interaction at the level of the machine designers (the society of designers) and the level of the machine interaction itself (the resulting artificial society). Taking into account such attributes of the artificial society as efficiency, and the self-interest of each member in the society of designers, it analyzes what kinds of rules should be instituted to govern interaction among these autonomous agents.
The authors point out that adjusting the rules of public behavior—or the rules of the game—by which the programs must interact can influence the private strategies that designers set up in their machines, shaping design choices and run-time behavior, as well as social behavior.
Artificial Intelligence series
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Machines That Make Deals
Task Oriented Domains
State Oriented Domains
Worth Oriented Domains
A StrictTolerant Mechanisms
Both Goals Are Achievable Alone
achieve his goal agree agreement all-or-nothing deals Artificial Intelligence assume automated agents behavior benefit blocks in slot c(Ti coin toss compromise situation concave TOD conflict situation consider cooperative situation coordination cost function deal that gives Decoy Lies decoy tasks definition deliver Distributed Artificial Intelligence efficient encounter equilibrium example exists expected utility game theory gives agent graph Incentive Compatibility individual rational initial interaction maximizes the product Min-Sufficient Strategy minimal mixed deals mixed joint plan modular Monotonic Concession Protocol multi-agent multi-agent systems multi-plan deals Nash equilibrium negotiation mechanism negotiation set operations pareto optimal player possible deals Postmen Domain product maximizing Proof of Theorem pure deals reach role satisfies both agents Section semi-cooperative deals set of tasks social welfare function stable stand-alone cost Strict and Tolerant subadditive TOD subgoals Task Oriented Domains Tolerant mechanisms transfer utility two-agent warehouse worth function Zeuthen Strategy
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