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Towards Decentralizationby

Matti Saastamoinen

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

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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

Introduction

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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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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

Introduction

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

Agent communications

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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

Agent communications

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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:

(=> (and (real-number ?x) (even-number ?n)) (> (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 system’s overall state is dispersed throughout the system commitments are viewed as pledges to undertake a specified course of actions

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

Coordination protocols

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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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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

Cooperative protocols

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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.

Cooperative protocols

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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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• 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

Result Sharing

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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 (can’t 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 agent’s 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 agent’s 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 player’s 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 -> Changeability

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thank you for your attention!