44,643 research outputs found
Bounded Decentralised Coordination over Multiple Objectives
We propose the bounded multi-objective max-sum algorithm (B-MOMS), the first decentralised coordination algorithm for multi-objective optimisation problems. B-MOMS extends the max-sum message-passing algorithm for decentralised coordination to compute bounded approximate solutions to multi-objective decentralised constraint optimisation problems (MO-DCOPs). Specifically, we prove the optimality of B-MOMS in acyclic constraint graphs, and derive problem dependent bounds on its approximation ratio when these graphs contain cycles. Furthermore, we empirically evaluate its performance on a multi-objective extension of the canonical graph colouring problem. In so doing, we demonstrate that, for the settings we consider, the approximation ratio never exceeds 2, and is typically less than 1.5 for less-constrained graphs. Moreover, the runtime required by B-MOMS on the problem instances we considered never exceeds 30 minutes, even for maximally constrained graphs with agents. Thus, B-MOMS brings the problem of multi-objective optimisation well within the boundaries of the limited capabilities of embedded agents
Coordinated constraint relaxation using a distributed agent protocol
The interactions among agents in a multi-agent system for coordinating a distributed,
problem solving task can be complex, as the distinct sub-problems of the individual
agents are interdependent. A distributed protocol provides the necessary framework for
specifying these interactions. In a model of interactions where the agents' social norms
are expressed as the message passing behaviours associated with roles, the dependencies
among agents can be specified as constraints. The constraints are associated with roles to
be adopted by agents as dictated by the protocol. These constraints are commonly
handled using a conventional constraint solving system that only allows two satisfactory
states to be achieved - completely satisfied or failed. Agent interactions then become
brittle as the occurrence of an over-constrained state can cause the interaction between
agents to break prematurely, even though the interacting agents could, in principle, reach
an agreement. Assuming that the agents are capable of relaxing their individual
constraints to reach a common goal, the main issue addressed by this thesis is how the
agents could communicate and coordinate the constraint relaxation process. The
interaction mechanism for this is obtained by reinterpreting a technique borrowed from
the constraint satisfaction field, deployed and computed at the protocol level.The foundations of this work are the Lightweight Coordination Calculus (LCC) and
the distributed partial Constraint Satisfaction Problem (CSP). LCC is a distributed
interaction protocol language, based on process calculus, for specifying and executing
agents' social norms in a multi-agent system. Distributed partial CSP is an extension of
partial CSP, a means for managing the relaxation of distributed, over-constrained, CSPs.
The research presented in this thesis concerns how distributed partial CSP technique,
used to address over-constrained problems in the constraint satisfaction field, could be
adopted and integrated within the LCC to obtain a more flexible means for constraint
handling during agent interactions. The approach is evaluated against a set of overconstrained Multi-agent Agreement Problems (MAPs) with different levels of hardness.
Not only does this thesis explore a flexible and novel approach for handling constraints
during the interactions of heterogeneous and autonomous agents participating in a
problem solving task, but it is also grounded in a practical implementation
Cost Adaptation for Robust Decentralized Swarm Behaviour
Decentralized receding horizon control (D-RHC) provides a mechanism for
coordination in multi-agent settings without a centralized command center.
However, combining a set of different goals, costs, and constraints to form an
efficient optimization objective for D-RHC can be difficult. To allay this
problem, we use a meta-learning process -- cost adaptation -- which generates
the optimization objective for D-RHC to solve based on a set of human-generated
priors (cost and constraint functions) and an auxiliary heuristic. We use this
adaptive D-RHC method for control of mesh-networked swarm agents. This
formulation allows a wide range of tasks to be encoded and can account for
network delays, heterogeneous capabilities, and increasingly large swarms
through the adaptation mechanism. We leverage the Unity3D game engine to build
a simulator capable of introducing artificial networking failures and delays in
the swarm. Using the simulator we validate our method on an example coordinated
exploration task. We demonstrate that cost adaptation allows for more efficient
and safer task completion under varying environment conditions and increasingly
large swarm sizes. We release our simulator and code to the community for
future work.Comment: Accepted to IEEE/RSJ International Conference on Intelligent Robots
and Systems (IROS), 201
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