1,355 research outputs found
Predicting the expected behavior of agents that learn about agents: the CLRI framework
We describe a framework and equations used to model and predict the behavior
of multi-agent systems (MASs) with learning agents. A difference equation is
used for calculating the progression of an agent's error in its decision
function, thereby telling us how the agent is expected to fare in the MAS. The
equation relies on parameters which capture the agent's learning abilities,
such as its change rate, learning rate and retention rate, as well as relevant
aspects of the MAS such as the impact that agents have on each other. We
validate the framework with experimental results using reinforcement learning
agents in a market system, as well as with other experimental results gathered
from the AI literature. Finally, we use PAC-theory to show how to calculate
bounds on the values of the learning parameters
Planning for Decentralized Control of Multiple Robots Under Uncertainty
We describe a probabilistic framework for synthesizing control policies for
general multi-robot systems, given environment and sensor models and a cost
function. Decentralized, partially observable Markov decision processes
(Dec-POMDPs) are a general model of decision processes where a team of agents
must cooperate to optimize some objective (specified by a shared reward or cost
function) in the presence of uncertainty, but where communication limitations
mean that the agents cannot share their state, so execution must proceed in a
decentralized fashion. While Dec-POMDPs are typically intractable to solve for
real-world problems, recent research on the use of macro-actions in Dec-POMDPs
has significantly increased the size of problem that can be practically solved
as a Dec-POMDP. We describe this general model, and show how, in contrast to
most existing methods that are specialized to a particular problem class, it
can synthesize control policies that use whatever opportunities for
coordination are present in the problem, while balancing off uncertainty in
outcomes, sensor information, and information about other agents. We use three
variations on a warehouse task to show that a single planner of this type can
generate cooperative behavior using task allocation, direct communication, and
signaling, as appropriate
Multi-Agent Game Abstraction via Graph Attention Neural Network
In large-scale multi-agent systems, the large number of agents and complex
game relationship cause great difficulty for policy learning. Therefore,
simplifying the learning process is an important research issue. In many
multi-agent systems, the interactions between agents often happen locally,
which means that agents neither need to coordinate with all other agents nor
need to coordinate with others all the time. Traditional methods attempt to use
pre-defined rules to capture the interaction relationship between agents.
However, the methods cannot be directly used in a large-scale environment due
to the difficulty of transforming the complex interactions between agents into
rules. In this paper, we model the relationship between agents by a complete
graph and propose a novel game abstraction mechanism based on two-stage
attention network (G2ANet), which can indicate whether there is an interaction
between two agents and the importance of the interaction. We integrate this
detection mechanism into graph neural network-based multi-agent reinforcement
learning for conducting game abstraction and propose two novel learning
algorithms GA-Comm and GA-AC. We conduct experiments in Traffic Junction and
Predator-Prey. The results indicate that the proposed methods can simplify the
learning process and meanwhile get better asymptotic performance compared with
state-of-the-art algorithms.Comment: Accepted by AAAI202
A Goal Driven Framework for Service Discovery in Service-Oriented Architecture: A Multiagent Based Approach
Automated service discovery is one of the very important features in any Semantic Web Service (SWS) based framework. Achieving this functionality in e-resource sharing system is not an easy task due to its hugeness and heterogeneity among the available resources. Any efficient automated service discovery will remain worthless until discovered services fulfill the required goal(s) demanded by the user or the client program. In this paper we have proposed a goal driven approach towards an automated service discovery using Agent Swarm in an innovative way .A novel multi agent based architecture has been introduced here for service discovery. Communications among the agent in service-oriented framework for the said purpose has also been illustrated here. Finally, the pictorial view of the running agent in the system is shown
Influence-Optimistic Local Values for Multiagent Planning --- Extended Version
Recent years have seen the development of methods for multiagent planning
under uncertainty that scale to tens or even hundreds of agents. However, most
of these methods either make restrictive assumptions on the problem domain, or
provide approximate solutions without any guarantees on quality. Methods in the
former category typically build on heuristic search using upper bounds on the
value function. Unfortunately, no techniques exist to compute such upper bounds
for problems with non-factored value functions. To allow for meaningful
benchmarking through measurable quality guarantees on a very general class of
problems, this paper introduces a family of influence-optimistic upper bounds
for factored decentralized partially observable Markov decision processes
(Dec-POMDPs) that do not have factored value functions. Intuitively, we derive
bounds on very large multiagent planning problems by subdividing them in
sub-problems, and at each of these sub-problems making optimistic assumptions
with respect to the influence that will be exerted by the rest of the system.
We numerically compare the different upper bounds and demonstrate how we can
achieve a non-trivial guarantee that a heuristic solution for problems with
hundreds of agents is close to optimal. Furthermore, we provide evidence that
the upper bounds may improve the effectiveness of heuristic influence search,
and discuss further potential applications to multiagent planning.Comment: Long version of IJCAI 2015 paper (and extended abstract at AAMAS
2015
Organisational Intelligence and Distributed AI
The analysis of this chapter starts from organisational theory, and from this it draws conclusions for the design, and possible organisational applications, of Distributed AI systems. We first review how the concept of organisations has emerged from non-organised "blackbox" entities to so-called "computerised" organisations. Within this context organisational researchers have started to redesign their models of intelligent organisations with respect to the availability of advanced computing technology. The recently emerged concept of Organisational Intelligence integrates these efforts in that it suggests five components of intelligent organisational skills (communication, memory, learning, cognition, problem solving). The approach integrates human and computer-based information processing and problem solving capabilities.<br/
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