Behavioral Anticipation in Agent Simulation

Cognitive Systems Research, 2012

The information processing paradigm advocated by integrate four functions -affect, motivation, cognition, and behavior -into three levels -reactive, routine, and reflective. However, it does not contain details of implementation. The three levels of information processing paradigm ties well with our view of two sources for inputs (or stimuli), namely external and internal inputs. This article provides an agent-based implementation of the three levels of information processing and clarifies the information flows of affect, motivation, cognition, behavior as well as perception. We simulate the three levels of information processing in a predator-prey environment. This paper shows manners in which affect, motivation, cognition, and behavior can be represented and used by agents at different levels of processing. This study contributes to understanding and processing of emotions, to three levels of information processing, and to predator-prey simulation.

Proceedings of the 44th annual southeast regional conference on - ACM-SE 44, 2006

Network fault management is concerned with the detection, isolation, and correction of anomalous conditions that occur in a computer network. Present state of art in fault management classifies existing methodologies into two main categories: reactive rule based approaches and intelligent monitoring systems. In this paper we explore the concept of anticipatory behavior to develop an intelligent agent-based network management model, which uses an anticipatory agent to proactively detect occurrence of faults using a predictive model pertaining to network performance. To compare the effectiveness of the anticipatory technique, we build a simulation model of a network using the DEVS framework. Two reactive rule based fault management strategies are compared against the anticipatory approach. Results of the comparative analysis are presented to demonstrate the potential of the anticipatory technique in detecting network anomalies.

The Challenge of Anticipation, 2008

Breakthrough challenges in the cognitive modeling are in providing artificial agents with abilities to operate in open systems where dynamism, partial knowledge and non-determinism exact agents not only to to quickly react to events but also to anticipate decisions, facing with uncertainty and unpredictability of future events. One of the key issues is then to enhance cognitive reasoning with emotions and affective abilities. The rationale behind the introduction of emotions into artificial agents is manifold. Since the grew up of ...

2007

We present two original systems in Artificial Life (ALife) domain, a new designed simulator World of Artificial Life (WAL) and an Anticipatory Agent System (AAS). Both of them are based on the concept of Multi Agent Systems (MAS). We made some modification in WAL basing on the anticipation behavior. Further, we use Genetic Algorithms (GA) within anticipatory module in ASS and evaluate the system. We have verified that anticipation behavior could be one of the most frequent behaviors in ALife, too. Our results confirmed that the system's performance is much better in this case.

2019

Dieses Paper prasentiert die IDEATE Architektur, eine auf den Sozialwissenschaften basierende Architektur zur Abbildung von Entscheidungsfindung und Verhalten von intelligenten Agenten. Besonderer Fokus der Architektur liegt auf der unmittelbaren Berucksichtigung von personlichen und gesellschaftlichen Uberzeugungen und Meinungen bei Entscheidungsfindung und der damit verbundenen Wechselwirkung von Agent und Umwelt. Daruber hinaus schlagt dieses Paper ein geeignetes Verfahren zur Formalisierung der Uberzeugungen und Meinungen zur Anwendung der IDEATE Architektur in einem spateren Simulationssystem vor.

2002

In this paper we argue that simulating complex systems involving human behaviour requires agent rules based on a theoretically rooted structure that captures basic behavioural processes. Essential components of such a structure involve needs, decision-making processes and learning. Such a structure should be based on state-of-the-art behavioural theories and validated on the micro-level using experimental or field data of individual behaviour. We provide some experiences we had working with such a structure, which involve the possibility to relate the results of simulations on different topics, the ease of building in extra factors for specific research questions and the possibility to use empirical data in calibrating the model. A disadvantage we experienced is the lack of suiting empirical data, which necessitates in our view the combined use of empirical and simulation research.

2000

Over the past few years, Multi-Agent Systems have become an important tool in computer science, particularly in the area of Artificial Intelligence and Simulation. Simulation is one of the major areas of application for multi-agent systems, bringing a new dimension to simulation by allowing us to directly represent individuals and their interaction. In fact it is the emphasis on the

Artificial Life

In May 2019, a workshop on principled development of future agent-based simulations was held at Keele University. Participants spanned companies and academia, and a range of domains of interest, as well as participant career stages. This report summarizes the discussions and main outcomes from this workshop.

Advances in Intelligent Systems and Computing, 2013

2005

In nearly any simulation system, there will be entities -that is, platforms or forces -that are not under the control of a human participant, either because the necessary personnel are not available or they would be too costly. These entities must mimic the behavior of the real-world entities that they represent in order to achieve some level of believability in the simulation. Crafting realistic, intelligent-seeming behaviors for simulated entities is a non-trivial task, however, and there are a variety of techniques that can be employed. While there exists a diverse array of agent architectures, two common approaches are cognitive architectures and state machines.