Model for Team Formation for Reformation in Multiagent Systems

ACM Transactions on Autonomous and Adaptive Systems, 2021

Team formation (TF) faces the problem of defining teams of agents able to accomplish a set of tasks. Resilience on TF problems aims to provide robustness and adaptability to unforeseen events involving agent deletion. However, agents are unaware of the inherent social welfare in these teams. This article tackles the problem of how teams can minimise their effort in terms of organisation and communication considering these dynamics. Our main contribution is twofold: first, we introduce the Stabilisable Team Formation (STF) as a generalisation of current resilient TF model, where a team is stabilisable if it possesses and preserves its inter-agent organisation from a graph-based perspective. Second, our experiments show that stabilisability is able to reduce the exponential execution time in several units of magnitude with the most restrictive configurations, proving that communication effort in subsequent task allocation problems are relaxed compared with current resilient teams. To ...

Using or extending Markov decision processes (MDPs) or partially observable Markov decision processes (POMDPs) to model multiagent decision problems has become an important trend. Generally speaking, there are two types of models: centralized ones and decentralized. The centralized ones focus on finding the best joint action given any global state, while the decentralied ones try to find out for each agent, what is the best local action given all the partial information available in that agent. Although decentralized models better capture the nature of decentralization in multiagent systems, they are much harder to solve compared to centralized models. In this paper, we show that, by studying the communication needs of the centralized models, we can establish a connection between the two models, and the solutions to centralized models (i.e. centralized policies) can be used to derive solutions to decentralized models (i.e. decentralized policies) -a process we call plan decomposition. We show that the amount of communication needed could be greatly reduced during the decomposition, and there are techniques that could be applied to produce a set of decentralized policies based on the same centralized policy. While this method does not solve decentralized models optimally, it does offer a great deal of flexibility and allows us to tradeoff the quality of the policies with the amount of communication needed, and gives us better insights about the need and timing for effective coordination in multiagent planning.

Today within the multiagent community, we see at least four competing methods to building multiagent systems: beliefdesire-intention (BDI), distributed constraint optimization (DCOP), distributed POMDPs, and auctions or game-theoretic methods. While there is exciting progress within each approach, there is a lack of cross-cutting research. This article highlights the various hybrid techniques for multiagent teamwork developed by the teamcore group. In particular, for the past decade, the TEAMCORE research group has focused on building agent teams in complex, dynamic domains. While our early work was inspired by BDI, we will present an overview of recent research that uses DCOPs and distributed POMDPs in building agent teams. While DCOP and distributed POMDP algorithms provide promising results, hybrid approaches allow us to use the complementary strengths of different techniques to create algorithms that perform better than either of their component algorithms alone. For example, in the BDI-POMDP hybrid approach, BDI team plans are exploited to improve POMDP tractability, and POMDPs improve BDI team plan performance.

Autonomous Agents & Multiagent Systems/International Conference on Autonomous Agents, 2004

Despite the recent advances in distributed MDP frameworks for reasoning about multiagent teams, these frameworks mostly do not reason about resource constraints, a crucial issue in teams. To address this shortcoming, we provide four key contributions. First, we introduce EMTDP, a distributed MDP framework where agents must not only maximize expected team reward, but must simultaneously bound expected resource consumption.

2009

In our work in Project 10, Task 1, we are investigating how agents can help teams to better perform their allocated tasks. Our work to date has developed models that construct plans for the members of a team, plans that allow the team to reach some specified team goal, and then extract from this the individual actions that team members have to perform. A key element of our work has been factoring into the plans, and the model that constructs the plans, the need for communication.

Autonomous Agents and Multi-Agent Systems, 2011

We consider a problem domain where coalitions of agents are formed in order to execute tasks. Each task is assigned at most one coalition of agents, and the coalition can be reorganized during execution. Executing a task means bringing it into one of the desired terminal states, which might take several time steps. The state of the task evolves even if no coalition is assigned to its execution and depends nondeterministically on the cumulative actions of the agents in the coalition. Furthermore, we assume that the reward obtained for executing a task evolves in time: typically, the more delay in the execution, the lesser the reward. We exemplify this class of problems by the allocation of firefighters to fires in a disaster rescue environment. We describe a practical methodology through which the aspects of this problem can be encoded as a Markov Decision Process. An experimental study involving the Robocup Rescue simulator shows that a coalition formation policy developed following our methodology outperforms heuristic approaches.

Proceedings of the second international joint conference on Autonomous agents and multiagent systems - AAMAS '03, 2003

Despite the success of the BDI approach to agent teamwork, initial role allocation (i.e. deciding which agents to allocate to key roles in the team) and role reallocation upon failure remain open challenges. What remain missing are analysis techniques to aid human developers in quantitatively comparing different initial role allocations and competing role reallocation algorithms. To remedy this problem, this paper makes three key contributions. First, the paper introduces RMTDP (Role-based Multiagent Team Decision Problem), an extension to MTDP [9], for quantitative evaluations of role allocation and reallocation approaches. Second, the paper illustrates an RMTDP-based methodology for not only comparing two competing algorithms for role reallocation, but also for identifying the types of domains where each algorithm is suboptimal, how much each algorithm can be improved and at what computational cost (complexity). Such algorithmic improvements are identified via a new automated procedure that generates a family of locally optimal policies for comparative evaluations. Third, since there are combinatorially many initial role allocations, evaluating each in RMTDP to identify the best is extremely difficult. Therefore, we introduce methods to exploit task decompositions among subteams to significantly prune the search space of initial role allocations. We present experimental results from two distinct domains.

2005

ABSTRACT Efficient coordination among large numbers of heterogeneous agents promises to revolutionize the way in which some complex tasks, such as responding to urban disasters can be performed. Token-based approaches have shown to be a novel and promising way for such coordination. However, previous token-based algorithms were built on heuristics and did not explicitly consider utilities related to token movements or changes in team states.

1999

Multi-agent domains consisting of teams of agents that need to collaborate in an adversarial environment offer challenging research opportunities. In this paper, we introduce periodic team synchronization domains, as time-critical environments in which agents act autonomously with limited communication, but they can periodically synchronize in a full-communication setting. We present a team agent structure that allows for an agent to capture and reason about team agreements.

Information & Software Technology, 2002

We present an approach for coordinating the actions of a team of real world autonomous agents on a high level. The method extends the framework of multiagent Markov decision processes with the notion of roles, a flexible and natural way to give each member of the team a clear de- scription of its task. A role in our framework defines