101 research outputs found
Automatic Image Annotation using Image Clustering in Multi – Agent Society
The rapid growth of the internet provides tremendous resource for
information in different domains (text, image, voice, and many others). This
growth introduces new challenge to hit an exact match due to huge number
of document returned by search engines where millions of items can be
returned for certain subject. Images have been important resources for
information, and billions of images are searched to fulfill user demands,
which face the mentioned challenge. Automatic image annotation is a
promising methodology for image retrieval. However most current
annotation models are not yet sophisticated enough to produce high quality
annotations. This thesis presents online intelligent indexing for image
repositories based on their contents, although content based indexing and
retrieving systems have been introduced, this thesis is adding an intelligent
technique to re-index images upon better understanding for its composed
concepts. Collaborative Agent scheme has been developed to promote
objects of an image to concepts and re-index it according to domain
specifications. Also this thesis presents automatic annotation system based
on the interaction between intelligent agents. Agent interaction is synonym
to socialization behavior dominating Agent society. The presented system is
exploiting knowledge evolution revenue due to the socialization to charge up
the annotation process
Learning to communicate in cooperative multi-agent reinforcement learning
Recent advances in deep reinforcement learning have produced unprecedented results. The success obtained on single-agent applications led to exploring these techniques in the context of multi-agent systems where several additional challenges need to be considered. Communication has always been crucial to achieving cooperation in multi-agent domains and learning to communicate represents a fundamental milestone for multi-agent reinforcement learning algorithms. In this thesis, different multi-agent reinforcement learning approaches are explored. These provide architectures that are learned end-to-end and capable of achieving effective communication protocols that can boost the system performance in cooperative settings. Firstly, we investigate a novel approach where intra-agent communication happens through a shared memory device that can be used by the agents to exchange messages through learnable read and write operations. Secondly, we propose a graph-based approach where connectivities are shaped by exchanging pairwise messages which are then aggregated through a novel form of attention mechanism based on a graph diffusion model. Finally, we present a new set of environments with real-world inspired constraints that we utilise to benchmark the most recent state-of-theart solutions. Our results show that communication can be a fundamental tool to overcome some of the intrinsic difficulties that characterise cooperative multi-agent systems
BNAIC 2008:Proceedings of BNAIC 2008, the twentieth Belgian-Dutch Artificial Intelligence Conference
The Ecology of Open-Ended Skill Acquisition: Computational framework and experiments on the interactions between environmental, adaptive, multi-agent and cultural dynamics
An intriguing feature of the human species is our ability to continuously invent new problems and to proactively acquiring new skills in order to solve them: what is called open-ended skill acquisition (OESA). Understanding the mechanisms underlying OESA is an important scientific challenge in both cognitive science (e.g. by studying infant cognitive development) and in artificial intelligence (aiming at computational architectures capable of open-ended learning). Both fields, however, mostly focus on cognitive and social mechanisms at the scale of an individual’s life. It is rarely acknowledged that OESA, an ability that is fundamentally related to the characteristics of human intelligence, has been necessarily shaped by ecological, evolutionary and cultural mechanisms interacting at multiple spatiotemporal scales. In this thesis, I present a research program aiming at understanding, modelingand simulating the dynamics of OESA in artificial systems, grounded in theories studying its eco-evolutionary bases in the human species. It relies on a conceptual framework expressing the complex interactions between environmental, adaptive, multi-agent and cultural dynamics. Three main research questions are developed and I present a selection of my contributions for each of them.- What are the ecological conditions favoring the evolution of skill acquisition?- How to bootstrap the formation of a cultural repertoire in populations of adaptive agents?- What is the role of cultural evolution in the open-ended dynamics of human skill acquisition?By developing these topics, we will reveal interesting relationships between theories in human evolution and recent approaches in artificial intelligence. This will lead to the proposition of a humanist perspective on AI: using it as a family of computational tools that can help us to explore and study the mechanisms driving open-ended skill acquisition in both artificial and biological systems, as a way to better understand the dynamics of our own species within its whole ecological context. This document presents an overview of my scientific trajectory since the start of my PhD thesis in 2007, the detail of my current research program, a selection of my contributions as well as perspectives for future work
Multi-agent Communication Protocols with Emergent Behaviour
The emergent behaviour of a multiagent system depends on the component agents and how
they interact. A critical part of interaction between agents is communication. This thesis
presents a multi-agent system communication model for physical moving agents. The work
presented in this thesis provides all the tools to create a physical multi-agent communication
system. The model integrates different agent technologies at both the micro and macro level.
The micro structure involves the architecture of the individual components in the system
whilst the macro structure involves the interaction relationships between these individual
components in the system.
Regarding the micro structure of the system, the model provides the description of a
novel hybrid BDI-Blackboard architectured agent that builds-in a hybrid of reactive and
deliberative agent. The macro structure of the system, provided by this model, provides
the operational specifications of the communication protocols. The thesis presents a theory
of communication that integrates an animal intelligence technique together with a cognitive
intelligence one. This results in a local co-ordination of movements, and global task coordination.
Accordingly, agents are designed to communicate with other agents in order to
coordinate their movements via a set of behavioural rules. These behavioural rules allow
a simple directed flocking behaviour to emerge. A flocking algorithm is used because it
satisfies a major objective, i.e. it has a real time response to local environmental changes
and minimises the cost of path planning. A higher level communication mechanism is
implemented for task distribution that is carried out via a blackboard conversation and
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negotiation process with a ground based controller. All the tasks are distributed as team
tasks. A novel utilization of speech acts as communication utterances through a blackboard
negotiation process is proposed.
In order to implement the proposed communication model, a virtual environment is
built that satisfies the realism of representing the agents, environment, and the sensors
as well as representing the actions. The virtual environment used in the work is built
as a semi-immersive full-scale environment and provides the visualisation tools required
to test, modify, compare and evaluate different behaviours under different conditions. The
visualization tools allow the user to visualize agents negotiations and interacting with them.
The 3D visualisation and simulation tools allow the communication protocol to be tested and
the emergent behaviour to be seen in an easy and understandable manner. The developed
virtual environment can be used as a toolkit to test different communication protocols and
different agent’s architecture in real time
Complex adaptive systems based data integration : theory and applications
Data Definition Languages (DDLs) have been created and used to represent data in programming languages and in database dictionaries. This representation includes descriptions in the form of data fields and relations in the form of a hierarchy, with the common exception of relational databases where relations are flat. Network computing created an environment that enables relatively easy and inexpensive exchange of data. What followed was the creation of new DDLs claiming better support for automatic data integration. It is uncertain from the literature if any real progress has been made toward achieving an ideal state or limit condition of automatic data integration. This research asserts that difficulties in accomplishing integration are indicative of socio-cultural systems in general and are caused by some measurable attributes common in DDLs. This research’s main contributions are: (1) a theory of data integration requirements to fully support automatic data integration from autonomous heterogeneous data sources; (2) the identification of measurable related abstract attributes (Variety, Tension, and Entropy); (3) the development of tools to measure them. The research uses a multi-theoretic lens to define and articulate these attributes and their measurements. The proposed theory is founded on the Law of Requisite Variety, Information Theory, Complex Adaptive Systems (CAS) theory, Sowa’s Meaning Preservation framework and Zipf distributions of words and meanings. Using the theory, the attributes, and their measures, this research proposes a framework for objectively evaluating the suitability of any data definition language with respect to degrees of automatic data integration.
This research uses thirteen data structures constructed with various DDLs from the 1960\u27s to date. No DDL examined (and therefore no DDL similar to those examined) is designed to satisfy the law of requisite variety. No DDL examined is designed to support CAS evolutionary processes that could result in fully automated integration of heterogeneous data sources. There is no significant difference in measures of Variety, Tension, and Entropy among DDLs investigated in this research. A direction to overcome the common limitations discovered in this research is suggested and tested by proposing GlossoMote, a theoretical mathematically sound description language that satisfies the data integration theory requirements. The DDL, named GlossoMote, is not merely a new syntax, it is a drastic departure from existing DDL constructs. The feasibility of the approach is demonstrated with a small scale experiment and evaluated using the proposed assessment framework and other means. The promising results require additional research to evaluate GlossoMote’s approach commercial use potential
CAMP-BDI: an approach for multiagent systems robustness through capability-aware agents maintaining plans
Rational agent behaviour is frequently achieved through the use of plans, particularly
within the widely used BDI (Belief-Desire-Intention) model for intelligent agents. As
a consequence, preventing or handling failure of planned activity is a vital component
in building robust multiagent systems; this is especially true in realistic environments,
where unpredictable exogenous change during plan execution may threaten intended
activities.
Although reactive approaches can be employed to respond to activity failure through
replanning or plan-repair, failure may have debilitative effects that act to stymie recovery
and, potentially, hinder subsequent activity. A further factor is that BDI agents typically
employ deterministic world and plan models, as probabilistic planning methods
are typical intractable in realistically complex environments. However, deterministic
operator preconditions may fail to represent world states which increase the risk of
activity failure.
The primary contribution of this thesis is the algorithmic design of the CAMP-BDI
(Capability Aware, Maintaining Plans) approach; a modification of the BDI reasoning
cycle which provides agents with beliefs and introspective reasoning to anticipate
increased risk of failure and pro-actively modify intended plans in response.
We define a capability meta-knowledge model, providing information to identify
and address threats to activity success using precondition modelling and quantitative
quality estimation. This also facilitates semantic-independent communication of capability
information for general advertisement and of dependency information - we define
use of the latter, within a structured messaging approach, to extend local agent algorithms
towards decentralized, distributed robustness. Finally, we define a policy based
approach for dynamic modification of maintenance behaviour, allowing response to
observations made during runtime and with potential to improve re-usability of agents
in alternate environments.
An implementation of CAMP-BDI is compared against an equivalent reactive system
through experimentation in multiple perturbation configurations, using a logistics
domain. Our empirical evaluation indicates CAMP-BDI has significant benefit if activity
failure carries a strong risk of debilitative consequence
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