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Complexity of Negotiation Tasks Abstraction & Analysis in Multi-Agent Planning

Explore the challenges faced by collaborating and self-interested agents negotiating for resources, requiring formal modeling, scalable protocols, and structural analysis for success. Discover the critical importance of exploiting problem structure to manage computational complexity in distributed planning scenarios. Example: NASA planning with staggering possibilities.

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Complexity of Negotiation Tasks Abstraction & Analysis in Multi-Agent Planning

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  1. Multi-Agent Planning

  2. Complexity of Negotiation Tasks Abstraction & Analysis Tasks as Agents negotiating for resources Self-interested Selfish Agents I need resource X …

  3. Complexity of Negotiation Tasks Abstraction & Analysis Tasks as Agents negotiating for resources Self-interested Collaborating Agents Self-interested Selfish Agents I need resource X … I need resource X, and it will enable other agents to use it later …

  4. Complexity of Negotiation Tasks Abstraction & Analysis Tasks as Agents negotiating for resources Global Welfare Oriented Collaborating Agents Self-interested Collaborating Agents Self-interested Selfish Agents I need resource X … I need resource X, and it will enable other agents to use it later … I need resource X, and it will allow us to improve our joint success …

  5. Complexity of Negotiation Tasks Abstraction & Analysis Tasks as Agents negotiating for resources Global Welfare Oriented Collaborating Agents Self-interested Collaborating Agents Self-interested Selfish Agents Distributed Constraint Satisfaction Distributed Planning with Conjunctive Goals Distributed (Hierarchical) Planning with Disjunctive Goals Technology Reusage

  6. Formal Modeling & Complexity Analysis More and more important! Distributed (Hierarchical) Planning with Disjunctive Goals Distributed Planning with Conjunctive Goals Distributed Constraint Satisfaction • As the negotiation systems are getting more complex, we need more and more advanced: • Formal problem modeling & complexity analysis, • Structural analysis, and • Development of scalable generic negotiation protocols

  7. Hybrid Scheduling& Planning Poly-time NP-complete PSPACE-complete EXPTIME-complete Exploiting Structure is Crucial! Scheduling In terms of worst-case computational complexity, mixed scheduling/planning is significantly harder than scheduling. Therefore, need to exploit problem structure to tame computational complexity.

  8. Example (Nasa) Planning is hard: find right sequence of actions 10 actions, 10! = 3 x 106 Contingency planning is really hard: 10224 possible plans! 10 x 92 x 84 x 78 x … x 2256

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