Model-Based Reflection for Agent Evolution

Adaptability is a key characteristic of intelligence. My research explores techniques for enabling software agents to adapt themselves as their functional requirements change incrementally. In the domain of manufacturing, for example, a software agent designed to assemble physical artifacts may be given a new goal of disassembling artifacts. As another example, in the internet domain, a software agent designed to browse some types of documents may be called upon to browse a document of another type. In particular, my research examines the use of reflection (an agent's knowledge and reasoning about itself) to accomplish evolution (incremental adaptation of an agent's capabilities). I have developed a language called TMKL (Task-Method-Knowledge Language) that enables modeling of an agent's composition and functioning. A TMKL model of an agent explicitly represents the tasks the agent addresses, the methods it applies, and the knowledge it uses. TMKL models are hierarchical, i.e., they represents tasks, methods and knowledge at multiple levels of abstraction. I have also developed a reasoning shell called REM (Reflective Evolutionary Mind) which provides support for the execution and evolution of agents represented in TMKL. REM employs a variety of strategies for evolving TMKL agents. Some of these strategies are purely model-based: knowledge of composition and functioning encoded in TMKL directly enables adaptation. REM also employs two traditional artificial intelligence and machine learning techniques: generative planning and reinforcement learning. The combination of model-based adaptation, generative planning, and reinforcement learning constitutes a mechanism for re ective agent evolution which is capable of addressing a variety of problems to which none of these individual approaches alone is suited. My research demonstrates the computational feasibility of this mechanism using experiments involving a variety of intelligent software agents in a variety of domains

Intelligent agent for formal modelling of temporal multi-agent systems

Software systems are becoming complex and dynamic with the passage of time, and to provide better fault tolerance and resource management they need to have the ability of self-adaptation. Multi-agent systems paradigm is an active area of research for modeling real-time systems. In this research, we have proposed a new agent named SA-ARTIS-agent, which is designed to work in hard real-time temporal constraints with the ability of self-adaptation. This agent can be used for the formal modeling of any self-adaptive real-time multi-agent system. Our agent integrates the MAPE-K feedback loop with ARTIS agent for the provision of self-adaptation. For an unambiguous description, we formally specify our SA-ARTIS-agent using Time-Communicating Object-Z (TCOZ) language. The objective of this research is to provide an intelligent agent with self-adaptive abilities for the execution of tasks with temporal constraints. Previous works in this domain have used Z language which is not expressive to model the distributed communication process of agents. The novelty of our work is that we specified the non-terminating behavior of agents using active class concept of TCOZ and expressed the distributed communication among agents. For communication between active entities, channel communication mechanism of TCOZ is utilized. We demonstrate the effectiveness of the proposed agent using a real-time case study of traffic monitoring system

Modelling Learning as Modelling

Economists tend to represent learning as a procedure for estimating the parameters of the "correct" econometric model. We extend this approach by assuming that agents specify as well as estimate models. Learning thus takes the form of a dynamic process of developing models using an internal language of representation where expectations are formed by forecasting with the best current model. This introduces a distinction between the form and content of the internal models which is particularly relevant for boundedly rational agents. We propose a framework for such model development which use a combination of measures: the error with respect to past data, the complexity of the model, the cost of finding the model and a measure of the model's specificity The agent has to make various trade-offs between them. A utility learning agent is given as an example

Agents for educational games and simulations

This book consists mainly of revised papers that were presented at the Agents for Educational Games and Simulation (AEGS) workshop held on May 2, 2011, as part of the Autonomous Agents and MultiAgent Systems (AAMAS) conference in Taipei, Taiwan. The 12 full papers presented were carefully reviewed and selected from various submissions. The papers are organized topical sections on middleware applications, dialogues and learning, adaption and convergence, and agent applications

Overview on agent-based social modelling and the use of formal languages

Transdisciplinary Models and Applications investigates a variety of programming languages used in validating and verifying models in order to assist in their eventual implementation. This book will explore different methods of evaluating and formalizing simulation models, enabling computer and industrial engineers, mathematicians, and students working with computer simulations to thoroughly understand the progression from simulation to product, improving the overall effectiveness of modeling systems.Postprint (author's final draft

Early aspects: aspect-oriented requirements engineering and architecture design

This paper reports on the third Early Aspects: Aspect-Oriented Requirements Engineering and Architecture Design Workshop, which has been held in Lancaster, UK, on March 21, 2004. The workshop included a presentation session and working sessions in which the particular topics on early aspects were discussed. The primary goal of the workshop was to focus on challenges to defining methodical software development processes for aspects from early on in the software life cycle and explore the potential of proposed methods and techniques to scale up to industrial applications