Hybrid BDI-POMDP Framework for Multiagent Teaming

Nair, Ranjit; Tambe, Milind

doi:10.1613/jair.1549

Cited by 44 publications

(33 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…O is the set of observations, and is the probabilistic observation function that describes the probability that an observer will observe that the environment has moved from one state to another under a particular action. The work of Nair and Tambe [47] on team formation is one interesting example that uses POM-DP.…”

Section: Perspective On the Environment In Cognitive Agent Systemsmentioning

confidence: 99%

Environment as a first class abstraction in multiagent systems

Weyns

Omicini

Odell

2006

Auton Agent Multi-Agent Syst

332

205

View full text Add to dashboard Cite

The current practice in multiagent systems typically associates the environment with resources that are external to agents and their communication infrastructure. Advanced uses of the environment include infrastructures for indirect coordination, such as digital pheromones, or support for governed interaction in electronic institutions. Yet, in general, the notion of environment is not well defined. Functionalities of the environment are often dealt with implicitly or in an ad hoc manner. This is not only poor engineering practice, it also hinders engineers to exploit the full potential of the environment in multiagent systems.In this paper, we put forward the environment as an explicit part of multiagent systems. We give a definition stating that the environment in a multiagent system is a first-class abstraction with dual roles: (1) the environment provides the surrounding conditions for agents to exist, which implies that the environment is an essential part of every multiagent system, and (2) the environment provides an exploitable design abstraction for building multiagent system applications. We discuss the responsibilities of such an environment in multiagent systems and we present a reference model for the environment that can serve as a basis for environment engineering. To illustrate the power of the environment as a design abstraction, we show how the environment is successfully exploited in a real world application. Considering the environment as a first-class abstraction in multiagent systems opens up new horizons for research and development in multiagent systems.

show abstract

Section: Perspective On the Environment In Cognitive Agent Systemsmentioning

confidence: 99%

Environment as a first class abstraction in multiagent systems

Weyns

Omicini

Odell

2006

Auton Agent Multi-Agent Syst

332

205

View full text Add to dashboard Cite

show abstract

“…Team formation is classically studied as the problem of selecting the team with maximum expected utility for a given task, considering a model of the capabilities of each agent [31,62]. Under such framework we could directly compute the expected utility of a team for a certain task.…”

Section: Related Workmentioning

confidence: 99%

Every team deserves a second chance: an extended study on predicting team performance

Marcolino

Lakshminarayanan

Nagarajan

et al. 2016

Auton Agent Multi-Agent Syst

View full text Add to dashboard Cite

Voting among different agents is a powerful tool in problem solving, and it has been widely applied to improve the performance in finding the correct answer to complex problems. We present a novel benefit of voting, that has not been observed before: we can use the voting patterns to assess the performance of a team and predict their final outcome. This prediction can be executed at any moment during problem-solving and it is completely domain independent. Hence, it can be used to identify when a team is failing, allowing an operator to take remedial procedures (such as changing team members, the voting rule, or increasing the allocation of resources). We present three main theoretical results: (1) we show a theoretical explanation of why our prediction method works; (2) contrary to what would be expected based on a simpler explanation using classical voting models, we show that we can make accurate predictions irrespective of the strength (i.e., performance) of the teams, and that in fact, the prediction can work better for diverse teams composed of different agents than uniform teams made of copies of the best agent; (3) we show that the quality of our prediction increases with the size of the action space. We perform extensive experimentation in two different domains: Computer Go and Ensemble Learning. In Computer Go, we obtain high Multi-Agent Syst (2017) 31:1003-1054 quality predictions about the final outcome of games. We analyze the prediction accuracy for three different teams with different levels of diversity and strength, and show that the prediction works significantly better for a diverse team. Additionally, we show that our method still works well when trained with games against one adversary, but tested with games against another, showing the generality of the learned functions. Moreover, we evaluate four different board sizes, and experimentally confirm better predictions in larger board sizes. We analyze in detail the learned prediction functions, and how they change according to each team and action space size. In order to show that our method is domain independent, we also present results in Ensemble Learning, where we make online predictions about the performance of a team of classifiers, while they are voting to classify sets of items. We study a set of classical classification algorithms from machine learning, in a data-set of hand-written digits, and we are able to make high-quality predictions about the final performance of two different teams. Since our approach is domain independent, it can be easily applied to a variety of other domains.

show abstract

“…The BDI architecture has better scalability since the state space in (PO)MDPs grows explosively when modeling complex application domains (e.g., SCADA systems). A hybrid BDI-POMDP framework [22] has been proposed for quantitatively analysing the teaming behaviours of agents in an uncertain environment. Different from this approach, our proposed approach embeds PGMs into the BDI architecture to model the uncertainty about the environment and reason about optimal decisions of agents.…”

Section: Related Workmentioning

confidence: 99%

Incorporating PGMs into a BDI Architecture

Chen

Hong

Liu

et al. 2013

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. In this paper, we present a hybrid BDI-PGM framework, in which PGMs (Probabilistic Graphical Models) are incorporated into a BDI (belief-desire-intention) architecture. This work is motivated by the need to address the scalability and noisy sensing issues in SCADA (Supervisory Control And Data Acquisition) systems. Our approach uses the incorporated PGMs to model the uncertainty reasoning and decision making processes of agents situated in a stochastic environment. In particular, we use Bayesian networks to reason about an agent's beliefs about the environment based on its sensory observations, and select optimal plans according to the utilities of actions defined in influence diagrams. This approach takes the advantage of the scalability of the BDI architecture and the uncertainty reasoning capability of PGMs. We present a prototype of the proposed approach using a transit scenario to validate its effectiveness.

show abstract

Hybrid BDI-POMDP Framework for Multiagent Teaming

Cited by 44 publications

References 45 publications

Environment as a first class abstraction in multiagent systems

Environment as a first class abstraction in multiagent systems

Every team deserves a second chance: an extended study on predicting team performance

Incorporating PGMs into a BDI Architecture

Contact Info

Product

Resources

About