A branch of Distributed Artificial Intelligence (DAI) focused on how multiple agents, each possessing partial knowledge or capabilities, can collaborate to solve a complex problem by dividing and conquering the work. In contrast to centralized problem-solving systems, DPS distributes the problem-solving process across several autonomous agents that communicate and coordinate to reach a common solution.
DPS Key Characteristics
DPS emphasizes the collaborative decomposition of problems, where each agent works on a subset of the problem and contributes its partial solution to the overall solution. This approach is especially valuable when:
- The problem is too large or complex for one agent to handle.
- The system must remain operational despite individual agent failures.
- There is a need for parallel processing or scalability.
DPS Core Functions
- Task Decomposition: Breaking the global problem into manageable sub-problems, either statically at design time or dynamically during execution.
- Agent Allocation: Assigning sub-tasks to specific agents based on capabilities, resources, or proximity to data.
- Coordination and Integration: Ensuring that agents’ local solutions are synchronized and contribute coherently to the overall objective.
- Conflict Management: Resolving contradictions or inconsistencies between local solutions that may arise during integration.
DPS Applications
DPS is widely applied in areas where coordinated, distributed reasoning improves performance and resilience:
- Sensor networks where nodes collaborate to detect environmental events
- Collaborative product design and distributed manufacturing
- Disaster response systems for real-time coordination
- Multi-robot teams that share mapping or exploration tasks
DPS Benefits
- Scalability through task delegation and agent autonomy
- Robustness in the face of partial system failures
- Efficiency from parallel computation and reduced bottlenecks
- Flexibility in adapting to dynamic or heterogeneous problem domains
DPS Challenges
- Communication overhead between agents, especially in real-time systems
- Coordination complexity to ensure all sub-solutions align correctly
- Trust and security in open or multi-organizational agent networks
- Adaptability when environmental conditions or task goals change mid-process
DPS is closely related to Multi-Agent Systems (MAS) and distributed computing, but it centers explicitly on solving complex problems through collaboration. Unlike MAS, which includes both cooperative and competitive behaviors, DPS assumes agents are working toward a shared objective. It also differs from distributed computing by embedding decision-making and reasoning into each node, not just computation.