Holonic multi-agent systems

A holonic multi-agent paradigm is proposed, where agents give up parts of their autonomy and merge into a super-agent"(a holon), that acts - when seen from the outside - just as a single agent again. We explore the spectrum of this new paradigm, ranging from definitorial issues over classification of possible application domains, an algebraic characterization of the merge operation, to implementational aspects: We propose algorithms for holon formation and on-line re-configuration. Based on some general criteria for the distinction between holonic and non-holonic domains, we examine domains suitable for holonic agents and sketch the implementation of holonic agents in these scenarios. Finally, a case study of a holonic agent system is presented in detail: TELETRUCK system is a fleet management system in the transportation domain

Belief-Desire-Intention in RoboCup

The Belief-Desire-Intention (BDI) model of a rational agent proposed by Bratman has strongly influenced the research of intelligent agents in Multi-Agent Systems (MAS). Jennings extended Bratman’s concept of a single rational agent into MAS in the form of joint-intention and joint-responsibility. Kitano et al. initiated RoboCup Soccer Simulation as a standard problem in MAS analogous to the Blocks World problem in traditional AI. This has motivated many researchers from various areas of studies such as machine learning, planning, and intelligent agent research. The first RoboCup team to incorporate the BDI concept is ATHumboldt98 team by Burkhard et al. In this thesis we present a novel collaborative BDI architecture modeled for RoboCup 2D Soccer Simulation called the TA09 team which is based on Bratman’s rational agent, influenced by Cohen and Levesque’s commitment, and incorporating Jennings’ joint-intention. The TA09 team features observation-based coordination, layered planning, and dynamic formation positioning

TOKEN-BASED APPROACH FOR SCALABLE TEAMCOORDINATION

To form a cooperative multiagent team, autonomous agents are required to harmonize activities and make the best use of exclusive resources to achieve their common goal. In addition, to handle uncertainty and quickly respond to external environmental events, they should share knowledge and sensor in formation. Unlike small team coordination, agents in scalable team must limit the amount of their communications while maximizing team performance. Communication decisions are critical to scalable-team coordination because agents should target their communications, but these decisions cannot be supported by a precise model or by complete team knowledge.The hypothesis of my thesis is: local routing of tokens encapsulating discrete elements of control, based only on decentralized local probability decision models, will lead to efficient scalable coordination with several hundreds of agents. In my research, coordination controls including all domain knowledge, tasks and exclusive resources are encapsulated into tokens. By passing tokens around, agents transfer team controls encapsulated in the tokens. The team benefits when a token is passed to an agent who can make use of it, but communications incur costs. Hence, no single agent has sole responsible over any shared decision. The key problem lies in how agents make the correct decisions to target communications and pass tokens so that they will potentially benefit the team most when considering communication costs.My research on token-based coordination algorithm starts from the investigation of random walk of token movement. I found a little increase of the probabilities that agents make the right decision to pass a token, the overall efficiency of the token movement could be greatly enhanced. Moreover, if token movements are modeled as a Markov chain, I found that the efficiency of passing tokens could be significantly varied based on different network topologies.My token-based algorithm starts at the investigation of each single decision theoretic agents. Although under the uncertainties that exist in large multiagent teams, agents cannot act optimal, it is still feasible to build a probability model for each agents to rationally pass tokens. Specifically, this decision only allow agent to pass tokens over an associate network where only a few of team members are considered as token receiver.My proposed algorithm will build each agent's individual decision model based on all of its previously received tokens. This model will not require the complete knowledge of the team. The key idea is that I will make use of the domain relationships between pairs of coordination controls. Previously received tokens will help the receiver to infer whether the sender could benefit the team if a related token is received. Therefore, each token is used to improve the routing of other tokens, leading to a dramatic performance improvement when more tokens are added. By exploring the relationships between different types of coordination controls, an integrated coordination algorithm will be built, and an improvement of one aspect of coordination will enhance the performance of the others

Discovering Strategic Behaviour of Multi-Agent Systems in Adversary Settings

Can specific behaviour strategies be induced from low-level observations of two adversary groups of agents with limited domain knowledge? This paper presents a domain-independent Multi-Agent Strategy Discovering Algorithm (MASDA), which discovers strategic behaviour patterns of a group of agents under the described conditions. The algorithm represents the observed multi-agent activity as a graph, where graph connections correspond to performed actions and graph nodes correspond to environment states at action starts. Based on such data representation, the algorithm applies hierarchical clustering and rule induction to extract and describe strategic behaviour. The discovered strategic behaviour is represented visually as graph paths and symbolically as rules. MASDA was evaluated on RoboCup. Both soccer experts and quantitative evaluation confirmed the relevance of the discovered behaviour patterns

Second Workshop on Modelling of Objects, Components and Agents

This report contains the proceedings of the workshop Modelling of Objects, Components, and Agents (MOCA'02), August 26-27, 2002.The workshop is organized by the 'Coloured Petri Net' Group at the University of Aarhus, Denmark and the 'Theoretical Foundations of Computer Science' Group at the University of Hamburg, Germany. The homepage of the workshop is: http://www.daimi.au.dk/CPnets/workshop02

Plan Acquisition Through Intentional Learning in BDI Multi-Agent Systems

Multi-Agent Systems (MAS), a technique emanating from Distributed Artificial Intelligence, is a suitable technique to study complex systems. They make it possible to represent and simulate both elements and interrelations of systems in a variety of domains. The most commonly used approach to develop the individual components (agents) within MAS is reactive agency. However, other architectures, like cognitive agents, enable richer behaviours and interactions to be captured and modelled. The well-known Belief-Desire-Intentions architecture (BDI) is a robust approach to develop cognitive agents and it can emulate aspects of autonomous behaviour and is thus a promising tool to simulate social systems. Machine Learning has been applied to improve the behaviour of agents both individually or collectively. However, the original BDI model of agency, is lacking learning as part of its core functionalities. To cope with learning, the BDI agency has been extended by Intentional Learning (IL) operating at three levels: belief adjustment, plan selection, and plan acquisition. The latter makes it possible to increase the agent’s catalogue of skills by generating new procedural knowledge to be used onwards. The main contributions of this thesis are: a) the development of IL in a fully-fledged BDI framework at the plan acquisition level, b) extending IL from the single-agent case to the collective perspective; and c) a novel framework that melts reactive and BDI agents through integrating both MAS and Agent-Based Modelling approaches, it allows the configuration of diverse domains and environments. Learning is demonstrated in a test-bed environment to acquire a set of plans that drive the agent to exhibit behaviours such as target-searching and left-handed wall-following. Learning in both decision strata, single and collective, is tested in a more challenging and socially relevant environment: the Disaster-Rescue problem

Distributed cognitive systems : proceedings of the VKS'97 Workshop

To intensify the contacts between cognitive scientists and researchers in Distributed Artificial Intelligence, we have organised the 1st German workshop on Distributed Cognitive Systems, VKS'97, in association with the 21st German Conference on Artificial Intelligence, KI'97. The workshop has been held in Freiburg on the 11th of September 1997. This document comprises the contributions of various research fields under the aspect of social, intelligent agents and gives an impression of future, inter-disciplinary collaboration