1,811 research outputs found
Multiagent cooperation for solving global optimization problems: an extendible framework with example cooperation strategies
This paper proposes the use of multiagent cooperation for solving global optimization problems through the introduction of a new multiagent environment, MANGO. The strength of the environment lays in itsflexible structure based on communicating software agents that attempt to solve a problem cooperatively. This structure allows the execution of a wide range of global optimization algorithms described as a set of interacting operations. At one extreme, MANGO welcomes an individual non-cooperating agent, which is basically the traditional way of solving a global optimization problem. At the other extreme, autonomous agents existing in the environment cooperate as they see fit during run time. We explain the development and communication tools provided in the environment as well as examples of agent realizations and cooperation scenarios. We also show how the multiagent structure is more effective than having a single nonlinear optimization algorithm with randomly selected initial points
Mechanisms for Automated Negotiation in State Oriented Domains
This paper lays part of the groundwork for a domain theory of negotiation,
that is, a way of classifying interactions so that it is clear, given a domain,
which negotiation mechanisms and strategies are appropriate. We define State
Oriented Domains, a general category of interaction. Necessary and sufficient
conditions for cooperation are outlined. We use the notion of worth in an
altered definition of utility, thus enabling agreements in a wider class of
joint-goal reachable situations. An approach is offered for conflict
resolution, and it is shown that even in a conflict situation, partial
cooperative steps can be taken by interacting agents (that is, agents in
fundamental conflict might still agree to cooperate up to a certain point). A
Unified Negotiation Protocol (UNP) is developed that can be used in all types
of encounters. It is shown that in certain borderline cooperative situations, a
partial cooperative agreement (i.e., one that does not achieve all agents'
goals) might be preferred by all agents, even though there exists a rational
agreement that would achieve all their goals. Finally, we analyze cases where
agents have incomplete information on the goals and worth of other agents.
First we consider the case where agents' goals are private information, and we
analyze what goal declaration strategies the agents might adopt to increase
their utility. Then, we consider the situation where the agents' goals (and
therefore stand-alone costs) are common knowledge, but the worth they attach to
their goals is private information. We introduce two mechanisms, one 'strict',
the other 'tolerant', and analyze their affects on the stability and efficiency
of negotiation outcomes.Comment: See http://www.jair.org/ for any accompanying file
Multiagent Learning Through Indirect Encoding
Designing a system of multiple, heterogeneous agents that cooperate to achieve a common goal is a difficult task, but it is also a common real-world problem. Multiagent learning addresses this problem by training the team to cooperate through a learning algorithm. However, most traditional approaches treat multiagent learning as a combination of multiple single-agent learning problems. This perspective leads to many inefficiencies in learning such as the problem of reinvention, whereby fundamental skills and policies that all agents should possess must be rediscovered independently for each team member. For example, in soccer, all the players know how to pass and kick the ball, but a traditional algorithm has no way to share such vital information because it has no way to relate the policies of agents to each other. In this dissertation a new approach to multiagent learning that seeks to address these issues is presented. This approach, called multiagent HyperNEAT, represents teams as a pattern of policies rather than individual agents. The main idea is that an agent’s location within a canonical team layout (such as a soccer team at the start of a game) tends to dictate its role within that team, called the policy geometry. For example, as soccer positions move from goal to center they become more offensive and less defensive, a concept that is compactly represented as a pattern. iii The first major contribution of this dissertation is a new method for evolving neural network controllers called HyperNEAT, which forms the foundation of the second contribution and primary focus of this work, multiagent HyperNEAT. Multiagent learning in this dissertation is investigated in predator-prey, room-clearing, and patrol domains, providing a real-world context for the approach. Interestingly, because the teams in multiagent HyperNEAT are represented as patterns they can scale up to an infinite number of multiagent policies that can be sampled from the policy geometry as needed. Thus the third contribution is a method for teams trained with multiagent HyperNEAT to dynamically scale their size without further learning. Fourth, the capabilities to both learn and scale in multiagent HyperNEAT are compared to the traditional multiagent SARSA(λ) approach in a comprehensive study. The fifth contribution is a method for efficiently learning and encoding multiple policies for each agent on a team to facilitate learning in multi-task domains. Finally, because there is significant interest in practical applications of multiagent learning, multiagent HyperNEAT is tested in a real-world military patrolling application with actual Khepera III robots. The ultimate goal is to provide a new perspective on multiagent learning and to demonstrate the practical benefits of training heterogeneous, scalable multiagent teams through generative encoding
Asimovian Adaptive Agents
The goal of this research is to develop agents that are adaptive and
predictable and timely. At first blush, these three requirements seem
contradictory. For example, adaptation risks introducing undesirable side
effects, thereby making agents' behavior less predictable. Furthermore,
although formal verification can assist in ensuring behavioral predictability,
it is known to be time-consuming. Our solution to the challenge of satisfying
all three requirements is the following. Agents have finite-state automaton
plans, which are adapted online via evolutionary learning (perturbation)
operators. To ensure that critical behavioral constraints are always satisfied,
agents' plans are first formally verified. They are then reverified after every
adaptation. If reverification concludes that constraints are violated, the
plans are repaired. The main objective of this paper is to improve the
efficiency of reverification after learning, so that agents have a sufficiently
rapid response time. We present two solutions: positive results that certain
learning operators are a priori guaranteed to preserve useful classes of
behavioral assurance constraints (which implies that no reverification is
needed for these operators), and efficient incremental reverification
algorithms for those learning operators that have negative a priori results
The Current State of Normative Agent-Based Systems
Recent years have seen an increase in the application of ideas from the social sciences to computational systems. Nowhere has this been more pronounced than in the domain of multiagent systems. Because multiagent systems are composed of multiple individual agents interacting with each other many parallels can be drawn to human and animal societies. One of the main challenges currently faced in multiagent systems research is that of social control. In particular, how can open multiagent systems be configured and organized given their constantly changing structure? One leading solution is to employ the use of social norms. In human societies, social norms are essential to regulation, coordination, and cooperation. The current trend of thinking is that these same principles can be applied to agent societies, of which multiagent systems are one type. In this article, we provide an introduction to and present a holistic viewpoint of the state of normative computing (computational solutions that employ ideas based on social norms.) To accomplish this, we (1) introduce social norms and their application to agent-based systems; (2) identify and describe a normative process abstracted from the existing research; and (3) discuss future directions for research in normative multiagent computing. The intent of this paper is to introduce new researchers to the ideas that underlie normative computing and survey the existing state of the art, as well as provide direction for future research.Norms, Normative Agents, Agents, Agent-Based System, Agent-Based Simulation, Agent-Based Modeling
Together we stand, Together we fall, Together we win: Dynamic Team Formation in Massive Open Online Courses
Massive Open Online Courses (MOOCs) offer a new scalable paradigm for
e-learning by providing students with global exposure and opportunities for
connecting and interacting with millions of people all around the world. Very
often, students work as teams to effectively accomplish course related tasks.
However, due to lack of face to face interaction, it becomes difficult for MOOC
students to collaborate. Additionally, the instructor also faces challenges in
manually organizing students into teams because students flock to these MOOCs
in huge numbers. Thus, the proposed research is aimed at developing a robust
methodology for dynamic team formation in MOOCs, the theoretical framework for
which is grounded at the confluence of organizational team theory, social
network analysis and machine learning. A prerequisite for such an undertaking
is that we understand the fact that, each and every informal tie established
among students offers the opportunities to influence and be influenced.
Therefore, we aim to extract value from the inherent connectedness of students
in the MOOC. These connections carry with them radical implications for the way
students understand each other in the networked learning community. Our
approach will enable course instructors to automatically group students in
teams that have fairly balanced social connections with their peers, well
defined in terms of appropriately selected qualitative and quantitative network
metrics.Comment: In Proceedings of 5th IEEE International Conference on Application of
Digital Information & Web Technologies (ICADIWT), India, February 2014 (6
pages, 3 figures
- …