Partial Global Planning (PGP)

PGP provides a structured process for managing interdependencies:

1. Interaction Detection: By comparing local plans, agents identify landmarks—key world states or actions that are relevant to multiple agents. Interactions are classified as:
   • Negative: Conflicts (resource contention, mutually exclusive goals).
   • Positive: Synergies (one agent's action helps another).
   • Neutral: Independent actions.
2. Interaction Resolution: For negative interactions, agents engage in local negotiation or apply coordination rules to modify their plans. This may involve:
   • Sequencing actions to avoid conflict.
   • Assigning resources to avoid contention.
   • Goal modification if a conflict is irreconcilable.

PGP is fundamentally a decentralized coordination model. There is no single controller or orchestrator dictating the global plan. Coordination is achieved through peer-to-peer communication. Execution is asynchronous; agents do not need to be perfectly synchronized. They act based on their current PG-Plan, which has already accounted for known interactions. This makes PGP robust to communication delays and individual agent failures, as the system can continue to function based on the last known consistent partial views.

PGP sits between fully centralized and purely reactive coordination models.

• Vs. Centralized Planning: PGP avoids the single point of failure and computational bottleneck of a central planner.
• Vs. Contract Net Protocol: While Contract Net is used for task allocation, PGP is used for plan harmonization. Agents in PGP already have their own tasks and plans; they coordinate to make those plans compatible.
• Vs. Blackboard Systems: Both use a shared structure (plans vs. blackboard), but PGP agents exchange structured plans, while Blackboard agents react opportunistically to hypotheses on the shared data space.
• Vs. Stigmergy: PGP uses explicit plan communication, while stigmergy relies on indirect coordination through environmental modifications.

Common Applications:

• Multi-robot coordination in logistics or search & rescue, where robots have individual missions but must avoid collisions and share map data.
• Distributed sensor networks coordinating data collection schedules to optimize coverage and battery life.
• Autonomous vehicle fleets negotiating merging and lane changes at intersections.

Inherent Challenges:

• Combinatorial Complexity: Detecting all potential interactions in large systems can be difficult.
• Communication Overhead: While less than centralized planning, significant plan exchange is still required.
• Guaranteeing Optimality: The emergent global plan may be suboptimal compared to a theoretically perfect centralized solution, but it is often a practical and robust trade-off.

The Contract Net Protocol is a classic task allocation mechanism that complements PGP's planning focus. It defines a structured negotiation protocol where:

• A manager agent announces a task via a call for proposals
• Contractor agents evaluate the task and submit bids
• The manager evaluates bids and awards a contract to the best bidder
• The contractor executes the task and reports results PGP agents could use Contract Net to allocate sub-tasks after identifying cooperative opportunities through plan merging, creating a hybrid coordination system.

Joint Intentions is a formal theory from philosophical logic and AI that provides a semantics for collaborative behavior. It defines when a team of agents has a joint persistent goal, characterized by:

• Mutual belief in the goal among team members
• A joint commitment to achieve the goal until it is mutually believed to be satisfied, unachievable, or irrelevant
• Mutual responsiveness in action selection PGP operationalizes this theory by providing a computational mechanism—plan exchange and merging—through which agents can establish and maintain the shared mental states required for joint action.

The Blackboard Pattern is an alternative coordination architecture where multiple specialized agents, called knowledge sources, asynchronously contribute to solving a problem by reading from and writing to a shared data structure—the blackboard. Contrasts with PGP:

• PGP: Proactive, plan-driven coordination with structured communication
• Blackboard: Reactive, data-driven coordination through a shared workspace
• PGP: Agents have explicit models of each other's plans
• Blackboard: Agents are typically unaware of each other, interacting only through the shared medium Both address complex problem-solving but through fundamentally different coordination paradigms.

Coordination Graphs are graphical models that decompose a global coordination problem into local interactions between pairs or small groups of agents. They enable efficient computation in multi-agent decision-making by:

• Representing the global payoff function as a sum of local payoff functions
• Each local function depends only on a subset of agents' actions
• Using message-passing algorithms like max-sum to find optimal joint actions This approach is highly complementary to PGP: while PGP identifies which agents need to coordinate (through plan interaction detection), coordination graphs provide a mechanism for how those identified subsets should optimize their interdependent decisions.