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2007 | Book

Cooperative Information Agents XI

11th International Workshop, CIA 2007, Delft, The Netherlands, September 19-21, 2007. Proceedings

Editors: Matthias Klusch, Koen V. Hindriks, Mike P. Papazoglou, Leon Sterling

Publisher: Springer Berlin Heidelberg

Book Series : Lecture Notes in Computer Science


About this book

These are the proceedings of the 11th International Workshop on Cooperative Information Agents (CIA 2007), held at the Delft University of Technology, The Netherlands, September 19–21, 2007. Intoday’sworldofubiquitouslyconnectedheterogeneousinformationsystems and computing devices, the intelligent coordination and provision of relevant added-value information at any time, anywhere is of key importance to a va- ety of applications. This challenge is envisioned to be coped with by means of appropriate intelligent and cooperative information agents. An information agent is a computational software entity that has access to one or multiple heterogeneous and geographically dispersed data and infor- tion sources. It pro-actively searches for and maintains information on behalf of its human users, or other agents preferably just in time. In other words, it is managing and overcoming the di?culties associated with information overload in open, pervasive information and service landscapes. Each component of a modern cooperative information system is represented by an appropriate intelligent information agent capable of resolving system and semantic heterogeneities in a given context on demand. Cooperative infor- tion agents are supposed to accomplish both individual and shared joint goals depending on the actual user preferences in line with given or deduced limits of time, budget and resources available. One major challenge of developing age- based intelligent information systems in open environments like the Internet and the Web is to balance the autonomy of networked data, information, and knowledge sources with the potential payo?s of leveraging them by the use of cooperative and intelligent information agents.

Table of Contents


Invited Contributions

Managing Sensors and Information Sources Using Semantic Matchmaking and Argumentation
Effective deployment and utilisation of limited and constrained intelligence resources — including sensors and other sources — is seen as a key issue in modern multinational coalition operations. In this talk, I will examine the application of semantic matchmaking and argumentation technologies to the management of these resources. I will show how ontologies and reasoning can be used to assign sensors and sources to meet the needs of missions, and show how argumentation can support the process of gathering and reasoning about uncertain evidence obtained from sensor probes.
Alun Preece
Towards a Delegation Framework for Aerial Robotic Mission Scenarios
The concept of delegation is central to an understanding of the interactions between agents in cooperative agent problem-solving contexts. In fact, the concept of delegation offers a means for studying the formal connections between mixed-initiative problem-solving, adjustable autonomy and cooperative agent goal achievement. In this paper, we present an exploratory study of the delegation concept grounded in the context of a relatively complex multi-platform Unmanned Aerial Vehicle (UAV) catastrophe assistance scenario, where UAVs must cooperatively scan a geographic region for injured persons. We first present the scenario as a case study, showing how it is instantiated with actual UAV platforms and what a real mission implies in terms of pragmatics. We then take a step back and present a formal theory of delegation based on the use of 2APL and KARO. We then return to the scenario and use the new theory of delegation to formally specify many of the communicative interactions related to delegation used in achieving the goal of cooperative UAV scanning. The development of theory and its empirical evaluation is integrated from the start in order to ensure that the gap between this evolving theory of delegation and its actual use remains closely synchronized as the research progresses. The results presented here may be considered a first iteration of the theory and ideas.
P. Doherty, John-Jules Ch. Meyer
Analysis of Negotiation Dynamics
The process of reaching an agreement in a bilateral negotiation to a large extent determines that agreement. The tactics of proposing an offer and the perception of offers made by the other party determine how both parties engage each other and, as a consequence, the kind of agreement they will establish. It thus is important to gain a better understanding of the tactics and potential other factors that play a role in shaping that process. A negotiation, however, is typically judged by the efficiency of the outcome. The process of reaching an outcome has received less attention in literature and the analysis of the negotiation process is typically not as rigorous nor is it based on formal tools. Here we present an outline of a formal toolbox to analyze and study the dynamics of negotiation based on an analysis of the types of moves parties to a negotiation can make while exchanging offers. This toolbox can be used to study both the performance of human negotiators as well as automated negotiation systems.
Koen Hindriks, Catholijn M. Jonker, Dmytro Tykhonov
Multi-agent Learning Dynamics: A Survey
In this paper we compare state-of-the-art multi-agent reinforcement learning algorithms in a wide variety of games. We consider two types of algorithms: value iteration and policy iteration. Four characteristics are studied: initial conditions, parameter settings, convergence speed, and local versus global convergence. Global convergence is still difficult to achieve in practice, despite existing theoretical guarantees. Multiple visualizations are included to provide a comprehensive insight into the learning dynamics.
H. Jaap van den Herik, D. Hennes, M. Kaisers, K. Tuyls, K. Verbeeck

Information Search and Processing

An Architecture for Hybrid P2P Free-Text Search
Recent advances in peer to peer (P2P) search algorithms have presented viable structured and unstructured approaches for full-text search. We posit that these existing approaches are each best suited for different types of queries. We present PHIRST, the first system to facilitate effective full-text search within P2P networks. PHIRST works by effectively leveraging between the relative strengths of these approaches. Similar to structured approaches, agents first publish terms within their stored documents. However, frequent terms are quickly identified and not exhaustively stored, resulting in a significantly reduction in the system’s storage requirements. During query lookup, agents use unstructured searches to compensate for the lack of fully published terms. Additionally, they explicitly weigh between the costs involved with structured and unstructured approaches, allowing for a significant reduction in query costs. We evaluated the effectiveness of our approach using both real-world and artificial queries. We found that in most situations our approach yields near perfect recall. We discuss the limitations of our system, as well as possible compensatory strategies.
Avi Rosenfeld, Claudia V. Goldman, Gal A. Kaminka, Sarit Kraus
Multi-agent Cooperative Planning and Information Gathering
In this paper we propose a multi-agent architecture, made of co-operative information agents, where agents can share with one another their knowledge of the environment and expertise in planning for achieving goals. In particular we consider how through communication such agents can incrementally learn partial and full plans. Such information exchange is particularly useful in the case of situated agents which have diverse abilities and expertise and which have partial views of their environments. It is also useful in the case of agent systems where agents collaborate towards achieving joint or individual goals. We describe an agent model based on abductive logic programming and give detailed protocols and policies of communication. We then define formally what it means for such information exchanges to be effective, and prove results regarding termination and effectiveness of dialogues based on the formalized policies.
Fariba Sadri
Using Distributed Data Mining and Distributed Artificial Intelligence for Knowledge Integration
In this paper we study Distributed Data Mining from a Distributed Artificial Intelligence perspective. Very often, databases are very large to be mined. Then Distributed Data Mining can be used for discovering knowledge (rule sets) generated from parts of the entire training data set. This process requires cooperation and coordination between the processors because incon-sistent, incomplete and useless knowledge can be generated, since each processor uses partial data. Cooperation and coordination are important issues in Distributed Artificial Intelligence and can be accomplished with different techniques: planning (centralized, partially distributed and distributed), negotiation, reaction, etc. In this work we discuss a coordination protocol for cooperative learning agents of a MAS developed previously, comparing it conceptually with other learning systems. This cooperative process is hierarchical and works under the coordination of a manager agent. The proposed model aims to select the best rules for integration into the global model without, however, decreasing its accuracy rate. We have also done experiments comparing accuracy and complexity of the knowledge generated by the cooperative agents.
Ana C. M. P. de Paula, Bráulio C. Ávila, Edson Scalabrin, Fabrício Enembreck
Quantifying the Expected Utility of Information in Multi-agent Scheduling Tasks
In this paper we investigate methods for analyzing the expected value of adding information in distributed task scheduling problems. As scheduling problems are NP-complete, no polynomial algorithms exist for evaluating the impact a certain constraint, or relaxing the same constraint, will have on the global problem. We present a general approach where local agents can estimate their problem tightness, or how constrained their local subproblem is. This allows these agents to immediately identify many problems which are not constrained, and will not benefit from sending or receiving further information. Next, agents use traditional machine learning methods based on their specific local problem attributes to attempt to identify which of the constrained problems will most benefit from human attention. We evaluated this approach within a distributed cTAEMS scheduling domain and found this approach was overall quite effective.
Avi Rosenfeld, Sarit Kraus, Charlie Ortiz


Agent-Based Traffic Control Using Auctions
Traffic management nowadays is one of the key challenges for cities. One drawback of traditional approaches for traffic management is that they do not consider the different valuations of waiting-time reduction of the drivers. These valuations can differ from driver to driver, e.g., drivers who are late for their job interview have a higher valuation of reduced waiting time than individuals driving home from work routinely. This also applies to trucks with urgent load, e.g., as part of a just-in-time production chain. To overcome this problem, we propose a new mechanism for traffic control at intersections called Initial Time-Slot Auction that is valuation-aware. It relies on agent-based driver-assistance systems to allocate the right to cross an intersection. Our evaluation shows that it does yield a significantly higher overall satisfaction.
Heiko Schepperle, Klemens Böhm
High-Performance Agent System for Intrusion Detection in Backbone Networks
This paper presents a design of high-performance agent-based intrusion detection system designed for deployment on high-speed network links. To match the speed requirements, wire-speed data acquisition layer is based on hardware-accelerated NetFlow like probe, which provides overview of current network traffic. The data is then processed by detection agents that use heterogenous anomaly detection methods. These methods are correlated by means of trust and reputation models, and the conclusions regarding the maliciousness of individual network flows is presented to the operator via one or more analysis agents, that automatically gather supplementary information about the potentially malicious traffic from remote data sources such as DNS, whois or router configurations. Presented system is designed to help the network operators efficiently identify malicious flows by automating most of the surveillance process.
Martin Rehák, Michal Pěchouček, Pavel Čeleda, Vojtěch Krmíček, Jiří Moninec, Tomáš Dymáček, David Medvigy
A MultiAgent System for Physically Based Rendering Optimization
Physically based rendering is the process of generating a 2D image from the abstract description of a 3D Scene. Despite the development of various new techniques and algorithms, the computational requirements of generating photorealistic images still do not allow to render in real time. Moreover, the configuration of good render quality parameters is very difficult and often too complex to be done by non-expert users. This paper describes a novel approach called MAgarRO (standing for “Multi-Agent AppRoach to Rendering Optimization”) which utilizes principles and techniques known from the field of multi-agent systems to optimize the rendering process. Experimental results are presented which show the benefits of MAgarRO-based rendering optimization.
Carlos Gonzalez-Morcillo, Gerhard Weiss, Luis Jimenez, David Vallejo, Javier Albusac
Neural Network Based Multiagent System for Simulation of Investing Strategies
Recent years of empirical research have collected enough evidences that for efficient markets the process of lower-wealth accumulation by capital investment is approximated by log-normal and high-wealth range by Pareto wealth distribution. This research aims to construct a simple neural network (NN) based multiagent system of heterogeneous agents’ targeted to get on the efficiency frontier by combining investments to the real life index funds and nonrisky financial assets, diversifying the risk and maximizing the profits. Each agent is represented by the different stock trading strategy according to his portfolio, saving and risk aversion preferences. The goal is, following empirical evidences from the real investment markets, to find enough proofs that NN-based multiagent system, in principle, has the same fundamental properties of real investment markets described by the log-normal, Pareto wealth and Levy stock returns distributions and can be used further to simulate even more complex social phenomena.
Darius Plikynas, Rimvydas Aleksiejūnas
Business Ecosystem Modelling: Combining Natural Ecosystems and Multi-Agent Systems
The increasing popularity of the “business ecosystem” concept in (business) strategy reflects that it is seen as one way to cope with increasingly dynamic and complex business environments. Nevertheless, the lack of a convincing model of a business ecosystem has led to the development of software which only give organisations a partial aid whilst neglecting their need for adaptation. Research in Multi-Agent Systems has proved to be suitable for modelling interactions among disparate sort of entities such as organisations. On the other hand, natural ecosystems continue to adapt themselves to changes in their dynamic and complex environments. In this paper, we present the Dynamic Agent-based Ecosystem Model. It combines ideas from natural ecosystems and multi-agent systems for business interactions.
César A. Marín, Iain Stalker, Nikolay Mehandjiev
From Local Search to Global Behavior: Ad Hoc Network Example
We introduce the Consensual N-Player Prisoner’s Dilemma as a large-scale dilemma. We then present a framework for cooperative consensus formation in large-scale MAS under the N-Person Prisoner’s Dilemma. Forming consensus is performed by demonstrating the applicability of a low-complexity physics-oriented approach to a large-scale ad hoc network problem. The framework is based on modeling cooperative MAS by a physics percolation theory. According to the model, agent-systems inherit physical properties, and therefore the evolution of the computational systems is similar to the evolution of physical systems. Specifically, we focus on the percolation theory, the emergence of self-organized criticality, and the exploitation of phase transitions. We provide a detailed low-ordered algorithm to be used by a single agent and implement this algorithm in our simulations. Via these approaches we demonstrate effective message delivery in a large-scale ad hoc network that consists of thousands of agents.
Osher Yadgar

Rational Cooperation

A Generic Framework for Argumentation-Based Negotiation
Past years have witnessed a growing interest in automated negotiation as a coordination mechanism for interacting agents. This paper presents a generic, problem- and domain-independent framework for argumentation-based negotiation that covers both essential agent-internal and external components relevant to automated negotiation. This framework, called Negotiation Situation Specification Scheme (N3S), is both suited as a guideline for implementing negotiation scenarios as well as integrating available approaches that address selective aspects of negotiation. In particular, N3S contributes to the state of the art in automated negotiation by identifying and relating basic argument types and negotiation stages in a structured and formal way.
Markus M. Geipel, Gerhard Weiss
The Effect of Mediated Partnerships in Two-Sided Economic Search
In this paper we investigate the effect of mediated partnerships over agents’ equilibrium strategies in two-sided economic search. A mediated partnership is formed when an agent acts as a mediator, establishing a partnership between a pair of agents it encountered along its search, thereby reducing the other agents’ amount of search. Surprisingly, this reduction in market friction induced by mediated partnerships does not always improve market efficiency. Use of mediated partnerships changes the equilibrium strategies used by agents in two-sided search models and introduces substantial computational complexity. This computational complexity is overcome with an innovative algorithm that facilitates equilibrium calculation.
Philip Hendrix, David Sarne
Who Works Together in Agent Coalition Formation?
Coalitions are often required for multi-agent collaboration. In this research, we consider tasks that can only be completed with the combined efforts of multiple agents using approaches which are both cooperative and competitive. Often agents forming coalitions determine optimal coalitions by looking at all possibilities. This requires an exponential algorithm and is not feasible when the number of agents and tasks is large. We propose agents use a two step process of first determining the task, and secondly, the agents that will be solicited to help complete the task. We describe polynomial time heuristics for each decision. We measure four different agent types using the described heuristics. We explore diminishing choices and performance under various parameters.
Vicki H. Allan, Kevin Westwood

Interaction and Cooperation

Using Ant’s Brood Sorting to Increase Fault Tolerance in Linda’s Tuple Distribution Mechanism
Coordination systems have been used in a variety of different applications but have never performed well in large scale, faulty settings. The sheer scale and level of complexity of today’s applications is enough to make the current ways of thinking about distributed systems (e.g. deterministic decisions about data organization) obsolete. All the same, computer scientists are searching for new approaches and are paying more attention to stochastic approaches that provide good solutions “most of the time”. The trade-off here is that by loosening certain requirements the system ends up performing better in other fronts such as adaptiveness to failures. Adaptation is a key component to fault-tolerance and tuple distribution is the center of the fault-tolerance problem in tuple-space systems. Hence, this paper shows how the tuple distribution in Linda-like systems can be solved by using an adaptive self-organized approach à la Swarm Intelligence. The results discussed in this paper demonstrate that efficient and adaptive solutions to this problem can be achieved using simple and inexpensive approaches.
Matteo Casadei, Ronaldo Menezes, Mirko Viroli, Robert Tolksdorf
Agent Behavior Alignment: A Mechanism to Overcome Problems in Agent Interactions During Runtime
When two or more agents interacting, their behaviors are not necessarily matching. Automated ways to overcome conflicts in the behavior of agents can make the execution of interactions more reliable. Such an alignment mechanism will reduce the necessary human intervention. This paper shows how to describe a policy for alignment, which an agent can apply when its behavior is in conflict with other agents. An extension of Petri Nets is used to capture the intended interaction of an agent in a formal way. Furthermore, a mechanism based on machine learning is implemented, to enable an agent to choose an appropriate alignment policy with collected problem information. Human intervention can reinforce certain successful policies in a given context, and can also contribute by adding completely new policies. Experiments have been conducted to test the applicability of the alignment mechanism and the main results are presented here.
Gerben G. Meyer, Nick B. Szirbik
Methods for Coalition Formation in Adaptation-Based Social Networks
Coalition formation in social networks consisting of a graph of interdependent agents allows many choices of which task to select and with whom to partner in the social network. Nodes represent agents and arcs represent communication paths for requesting team formation. Teams are formed in which each agent must be connected to another agent in the team by an arc. Agents discover effective network structures by adaptation. Agents also use several strategies for selecting the task and determining when to abandon an incomplete coalition. Coalitions are finalized in one-on-one negotiation, building a working coalition incrementally.
Levi Barton, Vicki H. Allan


Trust Modeling with Context Representation and Generalized Identities
We present a trust model extension that attempts to relax the assumptions that are currently taken by the majority of existing trust models: (i) proven identity of agents, (ii) repetitive interactions and (iii) similar trusting situations. The proposed approach formalizes the situation (context) and/or trusted agent identity in a multi-dimensional Identity-Context feature space, and attaches the trustworthiness evaluations to individual elements from this metric space, rather than to fixed identity tags (e.g. AIDs, addresses). Trustworthiness of the individual elements of the I-C space can be evaluated using any trust model that supports weighted aggregations and updates, allowing the integration of the mechanism with most existing work. Trust models with the proposed extension are appropriate for deployment in dynamic, ad-hoc and mobile environments, where the agent platform can’t guarantee the identity of the agents and where the cryptography-based identity management techniques may be too costly due to the unreliable and costly communication.
Martin Rehák, Michal Pěchouček
Learning Initial Trust Among Interacting Agents
Trust learning is a crucial aspect of information exchange, negotiation, and any other kind of social interaction among autonomous agents in open systems. But most current probabilistic models for computational trust learning lack the ability to take context into account when trying to predict future behavior of interacting agents. Moreover, they are not able to transfer knowledge gained in a specific context to a related context. Humans, by contrast, have proven to be especially skilled in perceiving traits like trustworthiness in such so-called initial trust situations. The same restriction applies to most multiagent learning problems. In complex scenarios most algorithms do not scale well to large state-spaces and need numerous interactions to learn. We argue that trust related scenarios are best represented in a system of relations to capture semantic knowledge. Following recent work on nonparametric Bayesian models we propose a flexible and context sensitive way to model and learn multidimensional trust values which is particularly well suited to establish trust among strangers without prior relationship. To evaluate our approach we extend a multiagent framework by allowing agents to break an agreed interaction outcome retrospectively. The results suggest that the inherent ability to discover clusters and relationships between clusters that are best supported by the data allows to make predictions about future behavior of agents especially when initial trust is involved.
Achim Rettinger, Matthias Nickles, Volker Tresp
A Probabilistic Framework for Decentralized Management of Trust and Quality
In this paper, we propose a probabilistic framework targeting three important issues in the computation of quality and trust in decentralized systems. Specifically, our approach addresses the multi-dimensionality of quality and trust, taking into account credibility of the collected data sources for more reliable estimates, while also enabling the personalization of the computation. We use graphical models to represent peers’ qualitative behaviors and exploit appropriate probabilistic learning and inference algorithms to evaluate their quality and trustworthiness based on related reports. Our implementation of the framework introduces the most typical quality models, uses the Expectation-Maximization algorithm to learn their parameters, and applies the Junction Tree algorithm to inference on them for the estimation of quality and trust. The experimental results validate the advantages of our approach: first, using an appropriate personalized quality model, our computational framework can produce good estimates, even with a sparse and incomplete recommendation data set; second, the output of our solution has well-defined semantics and useful meanings for many purposes; third, the framework is scalable in terms of performance, computation, and communication cost. Furthermore, our solution can be shown as a generalization or serve as the theoretical basis of many existing trust computational approaches.
Le-Hung Vu, Karl Aberer
Formal Analysis of Trust Dynamics in Human and Software Agent Experiments
Recognizing that trust states are mental states, this paper presents a formal analysis of the dynamics of trust in terms of the functional roles and representation relations for trust states. This formal analysis is done both in a logical framework and in a mathematical framework based on integral and differential equations. Furthermore, the paper presents formal specifications of a number of relevant dynamic properties of trust. The specifications provided were used to perform automated formal analysis of empirical and simulated data from two case studies, one involving two experiments with humans, and one involving simulation experiments in the context of an economic game.
Tibor Bosse, Catholijn M. Jonker, Jan Treur, Dmytro Tykhonov
Cooperative Information Agents XI
Matthias Klusch
Koen V. Hindriks
Mike P. Papazoglou
Leon Sterling
Copyright Year
Springer Berlin Heidelberg
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