Towards Decentralization

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Towards Decentralization by Matti Saastamoinen
12.02.2003
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Contents

• Introduction
• Multiagent systems
• Distriputed problem solving and planning
• Distributed Rational Decision Making
• Conclusions

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Introduction

• Book Multiagent Systems, A Modern Approach to
  Distributed Artificial Intelligence
• edited by Gerhard Weiss, TUM
• written year 1999
• many (22) authorities in the book
• introductory text and a textbook that covers the
  whole range of multiagent systems
• key concepts, methods and algorithms that form
  the core of the field
• extensive glossary

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Introduction

• artificial intelligence (AI) is the branch of
  computer science concerned with making computers
  behave like humans
• the term was first used in 1956 by John McCarthy
  at the Massachusetts Institute of Technology
• expert systems in the early 1980s
• the study of multiagent systems began in the
  field of distributed artificial intelligence
  (DAI) about 20 years ago
• greatest advances have occurred in the field of
  games playing (Deep Blue and Deep Junior)
• most common are LISP and Prolog

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Introduction

• artificial intelligence includes
• games playing programming computers to play
  games such as chess and checkers
• expert systems programming computers to make
  decisions in real-life situations
• natural language programming computers to
  understand natural human languages
• neural networks Systems that simulate
  intelligence by attempting to reproduce the types
  of physical connections that occur in animal
  brains
• robotics programming computers to see and hear
  and react to other sensory stimuli

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Multi-agent systems, motivations

• distributed computations are sometimes easier to
  understand and easier to develop, especially when
  the problem being solved is itself distributed
• there are also times when a centralized approach
  is impossible, because the systems and the data
  belong to independent organization that want to
  keep their information private and secure for
  competitive reason
• for the practical reason that the systems are
  too large and dynamic for global solutions to be
  formulated and implemented, the agents need to
  execute autonomously and be developed
  independently

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Characteristics of environments
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Agent communications

• agents to communicate single messages
• agents communicate in order to achieve better
  the goals of themselves or of the society/system
  in which they exits
• communication can enable the agents to
  coordinate their actions and behavior, resulting
  in systems that are more coherent
• coordination is a property of a system of agents
  performing some activity in a shared environment
• cooperation is coordination among no
  antagonistic agents
• negotiation is coordination among competitive or
  simply self-interested agents

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Agent communications

• a taxonomy of the different ways in which agents
  can coordinate their behavior and activities

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Agent communications

• three aspects to the formal study of
  communication
• syntax, how the symbols of communication are
  structured
• semantics, what the symbols denote
• pragmatics, how the symbols are interpreted

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KQML

• Knowledge Query and Manipulation Language (KQML)
  is a protocol and language for exchanging
  information and knowledge
• KQML-speaking agents appear to each other as
  clients and servers
• KQML is a protocol for communications among both
  agents and application programs

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KIF

• agents need descriptions of real-world things
• Knowledge Interchange Format (KIF), a particular
  logic language, has been proposed as a standard
  to use to describe things with in expert systems,
  databases, intelligent agents, etc.
• it is readable by both computer systems and
  people
• the expression shown below is an example of a
  complex sentence in KIF. It asserts that the
  number obtained by raising any real number ?x to
  an even power ?n is positive
• (gt (and (real-number ?x)
• (even-number ?n))
• (gt (expt ?x ?n) 0))

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Agent interaction protocols

• interaction protocols govern the exchange of a
  series of messages among agents a conversation
• agents can have conflicting goals or are simply
  self-interested, the objective of the protocols
  is to maximize the payoffs of the agents
• agents may have similar goals or common
  problems, the objective of the protocols is to
  maintain globally coherent performance of the
  agents without violating autonomy
• coordination and cooperation protocols

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Coordination protocols

• agents must coordinate their activities with
  each other to further their own interests or
  satisfy group goals
• there are dependencies between agents actions
• a need to meet global constraints
• no one agent has sufficient competence,
  resources or information to achieve system goals
• usually data and control is distributed
• disadvantage of distributing control and data is
  that knowledge of the systems overall state is
  dispersed throughout the system
• commitments are viewed as pledges to undertake a
  specified course of actions

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Coordination protocols

• conventions provide a means of managing
  commitments in changing circumstances
• commitments and conventions are the cornerstones
  of coordination

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Cooperative protocols

• a basic strategy is to decompose and then
  distribute tasks
• task decomposition might be done spatially,
  based on the layout of information sources or
  decision points, or functionally, according to
  the expertise of available tasks

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Cooperative protocols

• decomposed tasks distribution criteria
• avoid overloading critical resources
• assign tasks to agents with matching
  capabilities
• make an agent with a wide view assign tasks to
  other agents
• assign overlapping responsibilities to agents to
  achieve coherence
• assign highly interdependent tasks to agents in
  spatial or semantic proximity. This minimizes
  communication and synchronization costs
• reassign tasks if necessary for completing
  urgent tasks

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Cooperative protocols

• commonly used tasks distribution mechanisms are
• Market mechanisms tasks are matched to agents
  by generalized agreement or mutual selection
  (analogous to pricing commodities)
• Contract net announce, bid, and award cycles
• Multi-agent planning planning agents have the
  responsibility for task assignment
• Organizational structure agents have fixed
  responsibility for particular tasks.

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Distributed problem solving and planning

• emphasis is on getting agents to work together
  well to solve problems that require collective
  effort
• coherence agents need to want to work together
• competence agents need to know how to work
  together well
• distributed planning is tightly intertwined with
  distributed problem solving, being both a problem
  in itself and a means to solving a problem

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Motivations

• using distributed resources concurrently can
  allow a speedup of problem solving thanks to
  parallelism
• expertise or other problem-solving capabilities
  can be inherently distributed.
• beliefs or other data can be can be distributed
• the results of problem solving or planning might
  need to be distributed to be acted on by multiple
  agents

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Task Sharing

• decomposition Generate the set of tasks to
  potentially be passed others. This could
  generally involve decomposing large tasks into
  subtasks that could be tackled to different
  agents
• allocation Assign subtasks to appropriate agents
• accomplishment The appropriate agents each
  accomplish their subtasks, which could include
  further decomposition and subsubtask assigment,
  recursively to the point that an agent can
  accomplish the task it is handed alone
• result synthesis When an agent accomplish its
  subtask, it passes the result to the appropriate
  agent, who knows how to compose results into an
  overall solution

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Result Sharing

• can improve group performance in the following
  ways
• Confidence Independently derived results for
  the same task can be used to corroborate each
  other, yielding a collective result that has
  higher confidence of being correct
• Completeness Each agent formulates results for
  whichever subtasks it can accomplish, and these
  results altogether cover a more complete portion
  of the overall task
• Precision To refine its own solution, an agent
  needs to know more about the solutions that
  others have formulated

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Result Sharing

• Timeliness Even if an agent could in principle
  solve a large task alone, solving subtasks
  parallel can yield an overall solution faster
• difficulties in result sharing
• agents need to know what to do with shared
  results
• communicating large volumes of results can be
  costly
• as selective as possible about what to exchange

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Distributed Planning

• is something of an ambiguous term, because it is
  unclear exactly what is distributed, the
  planning or plans
• can be following kind of distributions
• centralized planning for distributed plans
• distributed planning for centralized plans
• distributed planning for distributed plans

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Distributed Planning and Execution

• post-planning coordination means that if one
  agent hits conflict (cant reach the goals) there
  are two ways to solve this conflict
• each agent formulates not only its expected
  plan, but also alternative plans to respond to
  possible contingencies that can arise at
  execution time
• through monitoring and replanning Each agent
  monitors its plan execution, and if there is a
  deviation it stops all agents progress, and the
  plan-coordination-execution cycle is repeated
• pre-planning coordination means that before an
  agent begins planning at all the conflict
  situations are found out

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Distributed Rational Decision Making

• automated negotiation systems with
  self-interested agents are becoming increasingly
  important
• each agent is trying to maximize its own good
  without concern for the global good
• save labor time of human negotiators
• other savings are possible because computational
  agents can be more effective at finding
  beneficial short-term contracts
• protocols need to be designed using a
  non-cooperative, strategic perspective
• robust non-manipulable multiagent systems, where
  the agents may be constructed by separate
  designer and/or may represent different real
  world parties.

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Evaluation Criteria

• negotiation protocols i.e. mechanisms can be
  evaluated according to many types of criteria
• the choice of protocol will then depend on what
  properties the protocol designer wants the
  overall system to have
• Social Welfare
• Pareto Efficiency
• Individual Rationality
• Stability
• Computational Efficiency
• Distribution and Communication Efficiency

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Voting

• plurality protocol
• binary protocol
• Borda protocol

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Auctions

• many practical computer science applications
• web sites exist for buying and selling items
  using auction protocols
• deal between two agents the auctioneer and one
  bidder
• auction settings private, common and correlated
  value auctions
• auction protocols
• English (first-price open-cry) auction
• the first-price sealed-bid auction
• Dutch (descending) auction
• Vickrey (second-price sealed-bid) auction
• all-pay auctions
• lookahead when auctioning items one at a time

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Bargaining

• agents can make a mutually beneficial agreement,
  but have a conflict of interest about which
  agreement to make
• axiomatic bargaining theory does not use the
  idea of a solution concept where the agents
  strategies form some type of equilibrium.
  Instead, desirable properties for a solution,
  called axioms of the bargaining solution, are
  postulated, and then the solution concept that
  satisfies these axioms is sought (Nash bargaining
  solution).
• strategic bargaining theory the bargaining
  situation is modeled as a game, and the solution
  concept is based on an analysis of which of the
  players strategies are in equilibrium

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General Equilibrium Market Mechanisms

• successfully adapted for and used in
  computational multiagent systems in many
  application domains
• provides a distributed method for efficiently
  allocating goods and resources
• two types of agents consumers and produces
• actual production and consumption only occur
  once the market has reached a general equilibrium
  
• most common decentralized algorithm for
  equilibrium search is the price tâtonnement
  process, which is a steepest descent search
  method
• use a single centralized mediator
• mediator might become a communication and
  computation bottleneck or a potential point of
  failure for the whole system

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Contract Nets

• formal model to a negotiation protocol that
  provably leads to desirable task allocation among
  agents
• contracting decisions are based on marginal cost
  calculations
• a contract is individually rational (IR) to an
  agent if that agent is better off with the
  contract than without it
• marginal cost is dynamic, it depends on the
  other tasks that the contractor already has
• a contractor is willing to allocate the task
  from its current task set to the contractor if it
  has to pay the contractor less than it save by
  handling the task itself
• accepting/rejecting decisions based on these
  marginal cost calculations
• the task allocation can only improve at each step

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Coalition Formation

• coalition formation is often studied in a more
  abstract setting called a characteristic function
  game (CFG)
• the value of each coalition is given by a
  characteristic function
• Coalition formation in CFGs includes three
  activities
• Coalition structure generation, with three
  agents, there are seven possible coalitions 1,
  2, 3, 1,2, 2,3, 1,3, 1,2,3 and five
  possible coalition structures 1, 2, 3,
  1, 2,3, 2, 1,3, 3, 1,2,
  1,2,3.
• Solving the optimization problem of each
  coalition
• Dividing the value of the generated solution
  among agents

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Conclusions

• distributed intelligent agents have a
  significant role in the future of software
  engineering!?
• further research is needed to develop the basis
  and techniques for societies of computational
  agents
• distributed planning has a variety of reasonable
  well-studied tools and techniques in it
  repertoire
• challenges is in characterizing these tools and
  understanding where and when to apply each
• goal of having heterogeneous plan generation and
  plan execution agents work together is likely to
  remain elusive
• representations and general-purpose strategies
  for distributed problem solving are even more
  elusive

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Conclusions

• distributed problem solving and planning
  strategy has still more art to it than we like
  to see in an engineering discipline
• real-time search provides an attractive
  framework, but there are still unresolved
  problems
• in the future, systems will increasingly be
  designed, built, and operated in a distributed
  manner
• a large number of systems will be used by
  multiple real-world parties (today internet)
• the problem of coordinating these parties and
  avoiding manipulation cannot be tackled by
  technological or economic methods alone
• the successful solutions are likely to emerge
  from a deep understanding and careful
  hybridization of both

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Conclusions

• centralized systems are failing for two simple
  reasons They can't scale, and they don't reflect
  the real world of people
• decentralization is neither automatic nor
  absolute
• the most decentralized system doesn't always win
• the challenge is to find the equilibrium
  points--the optimum group sizes, the viable
  models and the appropriate social compromises

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Conclusions

• Modularity Decentralization -gt Changeability

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thank you for your attention!