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

Agent programming in the cognitive era

It is claimed that, in the nascent ‘Cognitive Era’, intelligent systems will be trained using machine learning techniques rather than programmed by software developers. A contrary point of view argues that machine learning has limitations, and, taken in isolation, cannot form the basis of autonomous systems capable of intelligent behaviour in complex environments. In this paper, we explore the contributions that agent-oriented programming can make to the development of future intelligent systems. We briefly review the state of the art in agent programming, focussing particularly on BDI-based agent programming languages, and discuss previous work on integrating AI techniques (including machine learning) in agent-oriented programming. We argue that the unique strengths of BDI agent languages provide an ideal framework for integrating the wide range of AI capabilities necessary for progress towards the next-generation of intelligent systems. We identify a range of possible approaches to integrating AI into a BDI agent architecture. Some of these approaches, e.g., ‘AI as a service’, exploit immediate synergies between rapidly maturing AI techniques and agent programming, while others, e.g., ‘AI embedded into agents’ raise more fundamental research questions, and we sketch a programme of research directed towards identifying the most appropriate ways of integrating AI capabilities into agent programs

FIVE STEPS TO RESPONSIBILITY

Responsibility has entered the academic discourse of logicians hardly more than few decades ago. I suggest a logical concept of responsibility which employs ideas both from a number of theories belonging to different branches of logic as well from other academic areas. As a comment to this concept, I suggest five steps narrative scenario in order to show how the logical dimension of responsibility emerges from diverse tendencies in logic and other sciences. Here are the five steps briefly stated: Step 1. Developing modal formalisms capable of evaluative analysis of situations (deontic, epistemic and etc.). Step 2. Drawing a conceptual borderline between normal and non-normal (weak) logical systems. Step 3. Using different kinds of models. Step 4. Agent- and action- friendly turn in logic. Step 5. Creating formalisms for modeling different types of agency. An idea advocated here within 5-Steps route to responsibility is that this concept is a complex causal and evaluative (axiological) relation. A logical account may be given for causal and normative aspects of this relation. Unfolding the responsibility back and forth through 5 Steps will result in different concepts. The technicalities are minimized for the sake of keeping the philosophical scope of the paper. For the same reason I also refrain from discussing legal and juridical ramifications of the issue

Integration of social values in a multi-agent platform running in a supercomputer

Agent-based modelling is one of the most suitable ways to simulate and analyse complex problems and scenarios, especially those involving social interactions. Multi-agent systems, consisting of multiple agents in a simulation environment, are widely used to understand emergent behaviour in various fields such as sociology, economics and policy. However, existing multi-agent platforms often face challenges in terms of scalability and reasoning capacity. Some platforms can scale well in terms of computation, but lack sophisticated reasoning mechanisms. On the other hand, some platforms employ complex reasoning systems, but this can compromise their scalability. In this work, we have extended an existing platform developed at UPC that enables scalable, parallel HTN planning for complex agents. Our main goal has been to improve the analysis of social relationships between agents by incorporating moral values. Building on previous work done by David Marín on the implementation of the platform, we have made extensions and modifications both formally and in the implementation. We have formalised the additions to the system model and provided an updated implementation. Finally, we have presented a complex example scenario that demonstrates all the additions we have made. This scenario allows us to show how agents' preferences and moral values influence their decisions and actions in a simulated environment. Through this work, we have sought to improve the existing platform and fulfil the spirit and purpose of the platform