AGENTS AND THEIR KNOWLEDGE REPRESENTATIONS

In the paper we propose a Brain Architecture which allows developers of a Multi-Agent System (MAS) to integrate various supporting tools. For the purpose of agents' communication and reasoning we make use of Transparent Intesional logic (TIL), or more exactly of its software variant the TIL-Script language. The proposed architecture makes it possible to utilise also the Prolog language as a reasoning tool. Rules and facts are stored in agent's internal knowledge base is designed in a way appropriate both for Prolog and TIL-Script languages. The architecture is an open one so that other tools of reasoning can be easily incorporated.

JETAI, 2000

Autonomous agent architectures are design methodologies -collections of knowledge and strategies which are applied to the problem of creating situated intelligence. In this article, we attempt to integrate this knowledge across several architectural traditions. We pay particular attention to features which have tended to be selected under the pressure of extensive use in real-world systems. We conclude that the following strategies provide significant assistance in the design of autonomous intelligent control:

this paper presents a review of "How AI, cognitive science and DM are combined to develop intelligent agents", and how the paradigm first shifted from AI to Data mining and then towards combination of data mining and artificial intelligence. The paper will also provide a state-of-the-art account of the cognitive architectures. It also gives a detailed comparative study of all the architectures discussed in the paper. All the survey of data mining and cognitive architecture is done w.r.t Multi agent systems. Therefore, paper will also provide a bird eye view of MAS/ ABMS.

Cognitive Systems Research, 2018

In this paper we identify and characterize an analysis of two problematic aspects affecting the representational level of cognitive architectures (CAs), namely: the limited size and the homogeneous typology of the encoded and processed knowledge. We argue that such aspects may constitute not only a technological problem that, in our opinion, should be addressed in order to build artificial agents able to exhibit intelligent behaviours in general scenarios, but also an epistemological one, since they limit the plausibility of the comparison of the CAs' knowledge representation and processing mechanisms with those executed by humans in their everyday activities. In the final part of the paper further directions of research will be explored, trying to address current limitations and future challenges.

There has been increased interest recently in cognitive and behavior-based agent architectures and cognitive models of human behavior. This interest results in part from advances in agent technology, cognitive neuroscience and emotion research that make such models possible, and in part from maturing applications that require, or benefit from, the inclusion of different emotion-related aspects (e.g., adaptive human-computer interfaces, social and expressive robots, autonomous agents, decision support systems, etc). The objective of this paper is to present an architecture within which a large variety of modules/mechanisms are decomposed by function and structure and are interconnected by situated recomposition methods. The result of such recomposition is different types of perceptions, cognitive methods and behaviours. Another aspect is also presented which has not been commonly addressed in most architectures of this type: the distinction between cognition as a control structure fo...

Robotics and Autonomous Systems, 1998

An architecture for autonomous agents is proposed, that integrates the functional and the behavioral approaches to robotics. The integration is based on the introduction of a conceptual level, linking together a subconceptual, behavioral, level, and a linguistic level, encompassing symbolic representation and data processing. The proposed architecture is described with reference to an experimental setup, in which the robot task is that of building a significant description of its working environment.

Lecture Notes in Computer Science, 1999

The leading edge of computer science research is notoriously fickle. New trends come and go with alarming and unfailing regularity. In such a rapidly changing field, the fact that research interest in a subject lasts more than a year is worthy of note. The fact that, after five years, interest not only remains, but actually continues to grow is highly unusual. As 1998 marked the fifth birthday of the International Workshop on Agent Theories, Architectures, and Languages (ATAL), it seemed appropriate for the organizers of the original workshop to comment on this remarkable growth, and reflect on how the field has developed and matured. The first ATAL workshop was co-located with the Eleventh European Conference on Artificial Intelligence (ECAI-94), which was held in Amsterdam. The fact that we chose an AI conference to co-locate with is telling: at that time, we expected most researchers with an interest in agents to come from the AI community. The workshop, which was planned over the summer of 1993, attracted 32 submissions, and was attended by 55 people. ATAL was the largest workshop at ECAI-94, and the clear enthusiasm on behalf of the community made the decision to hold another ATAL workshop simple. The ATAL-94 proceedings were formally published in January 1995 under the title Intelligent Agents, and included an extensive review article, a glossary, a list of key agent systems, and-unusually for the proceedings of an academic workshop-a full subject index. The high scientific and production values embodied by the ATAL-94 proceedings appear to have been recognized by the community, and resulted in ATAL proceedings being the most successful sequence of books published in Springer-Verlag's Lecture Notes in Artificial Intelligence series. ATAL-95 was held at the International Joint Conference on AI, which in 1995 was held in Montreal, Canada. The number of submissions leapt to 55, and the workshop was attended by 70 people. Since many international conferences fail to attract this many submissions and delegates, it was decided at this point to make ATAL an annual event. It was also decided to hold ATAL-96 in Europe, following the successful model of ATAL-94 by co-locating with ECAI-96, which was held in Budapest, Hungary. We received 56 submissions, and the workshop was attended by about 60 delegates. For ATAL-97, it was felt that the workshop was sufficiently mature that it merited its own event, and so the conference was located immediately before the AAAI-97 conference in Providence, Rhode Island. It was attended by about 75 delegates. ATAL-98 was co-located with the "Agents World" series of events, held in Paris in July 1998. 90 submissions were received, and 139 delegates registered for ATAL. In the five years since ATAL-94, the landscape of the computing world has changed almost beyond recognition. Even seasoned veterans of the historically fast-moving IT environment have been surprised by the current rate of change. Perhaps the simplest way we can sum up these changes is by noting that the first ATAL was also the last not to have a World Wide Web (WWW) page. In 1999, on the eve of the new millennium, it would be unthinkable for a serious academic conference or workshop not to have a dedicated WWW site. The changes brought about by the explosion of the Internet into worldwide public and corporate awareness are well documented, and it is not appropriate for us to add to the mountain of comment (and hyperbole). However, it is important to note that the rise of the Internet had a significant impact on the development of the agent field VI Preface itself. By the summer of 1994 it was becoming clear that the Internet would be a major proving ground for agent technology (perhaps even the "killer application"), although the full extent of this interest was not yet apparent. The emergence of agents on and for the Internet gave rise to a new, associated software technology, somewhat distinct from the "mainstream" of agent research and development. In the summer of 1994, a California-based company called General Magic was creating intense interest in the idea of mobile agents-programs that could transmit themselves across an electronic network and recommence execution at a remote site. At the time, General Magic were distributing a widely-read white paper that described "Telescript"-a programming language intended to realize the vision of mobile agents. In the event, it was not Telescript, but another programming language that caught the imagination of the Internet community: Java. When Netscape incorporated a Java virtual machine into their Navigator browser, and hence brought the idea of applets into the mainstream, they gave Java an enormous impetus, both as a way of animating the Internet, but also as a powerful, well-designed object-oriented programming language in its own right. A number of mobile agent frameworks were rapidly developed and released as Java packages, and interest in Telescript rapidly waned. As we write this preface in late 1998, Java is the programming language of choice not just for agent systems, but also, it seems, for most other applications in computing. Mobile agent technology was not the only other agent technology beginning to make its presence felt at the time of the first ATAL. The summer of 1994 saw the publication of a special issue of Communications of the ACM with the title "intelligent agents". Many of the articles in this special issue described a new type of agent system, that acted as a kind of "expert assistant" to a user working with a particular class of application. The vision of agents as intelligent assistants was perhaps articulated most clearly by Pattie Maes from MIT Media Lab, who described a number of prototype systems to realize the vision. Such user interface agents rapidly caught the imagination of a wider community, and in particular, the commercial possibilities of such technologies was self-evident. A number of agent startup companies were founded to commercialize this technology (many of which have by now either been sold or gone bust). Current interest in such agents comes, to a great extent, from the possibility of using them in electronic commerce scenarios, where they negotiate on behalf of their "owner". The commercial interest in agents in the latter half of the 1990s has not been limited to venture capitalists and "small scale" agent systems. Perhaps one of the most encouraging long-term trends for agent technology is the idea of agents as a software engineering paradigm. The level of interest in this concept has been evidenced in several ways. For example, the number of large-scale industrial-strength agent systems being developed and deployed is an obvious indicator. However, the degree of interest is perhaps best illustrated by the attempts currently underway to develop international standards for agent communication. Although some tentative steps towards standard agent communication languages were taken by the KQML/KIF community in the USA in the early 1990s, it is the FIPA initiative, started in 1995, which currently appears to be the best candidate for a standard agent communication framework. Turning more specifically to the ATAL workshops, a number of smaller scale trends have emerged, echoing to some extent the more visible changes in the computing world Preface VII itself. One obvious indicator that agent technology is beginning to mature is that far fewer new agent architectures are being developed. It seems that authors are taking architectures off the shelf, rather than developing their own. In this vein, the belief-desire-intention (BDI) class of architectures has become particularly prominent. This work represents a paradigm example of theATAL ethos-there is a well-defined theory, which relates more or less directly to specific architectures or programming languages. On the theoretical side, there has been an increasing trend towards more integrated models; that is, theories which cover a wider proportion of an agent's decision making and acting capabilities. We noted above that five years sometimes seems like a long time for an academic workshop. Incredibly, when ATAL began, there were no conferences dedicated to agent technology. In contrast, the agent research community is now served by at least two major international scientific conferences (the International Conference on Multi-Agent Systems and the International Conference on Autonomous Agents), as well as a dedicated journal (Autonomous Agents and Multi-Agent Systems). That agent technology is able to comfortably support this degree of interest tells us that agents have a good chance of succeeding as a technology. We hope that ATAL will continue to play its part in this development, maintaining its current high level of scientific and production values, and serving a vibrant, rich research and development community. To close, we would like to take this opportunity to thank those who have made ATAL the success we sincerely believe it is today. In particular, our thanks go to Jörg Müller, Munindar Singh, Anand Rao, and Milind Tambe, who have all acted as organizers for ATAL, and helped to shape it through their dedication and vision. In addition, we would like to thank those who have played various other special roles throughout the first five years, including

2008

2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA), 2016

Due to their increasing complexity, the implementation of automation systems faces more challenges. Cognitive architectures have been designed to deal exactly with that. The purpose of a cognitive architecture is to find an action to every possible sensor data to move the system closer to a defined goal. The method is to utilize stored knowledge to reason about the best solution. The challenge for the implementation of such an architecture is to manage the overwhelming complexity of functionality and data. This paper presents the Agent-based Cognitive Architecture (ACONA) Framework, which aims at providing a general infrastructure, allowing the implementation of various cognitive architectures. It considers the encapsulation of functionality and provides a flexible composition of building blocks by applying a multi-agent approach.

This article addresses an open problem in the area of cognitive systems and architectures: namely the problem of handling (in terms of process- ing and reasoning capabilities) complex knowledge structures that can be at least plausibly comparable, both in terms of size and of typology of the encoded information, to the knowledge that humans process daily for executing everyday activities. Handling a huge amount of knowledge, and selectively retrieve it ac- cording to the needs emerging in different situational scenarios, is an important aspect of human intelligence. For this task, in fact, humans adopt a wide range of heuristics (Gigerenzer & Todd) due to their “bounded rationality” (Simon, 1957). In this perspective, one of the re- quirements that should be considered for the design, the realization and the evaluation of intelligent cognitively-inspired systems should be rep- resented by their ability of heuristically identify and retrieve, from the general knowledge stored in their artificial Long Term Memory (LTM), that one which is synthetically and contextually relevant. This require- ment, however, is often neglected. Currently, artificial cognitive systems and architectures are not able, de facto, to deal with complex knowledge structures that can be even slightly comparable to the knowledge heuris- tically managed by humans. In this paper I will argue that this is not only a technological problem but also an epistemological one and I will briefly sketch a proposal for a possible solution.

2017 International Artificial Intelligence and Data Processing Symposium (IDAP), 2017

Robotic agents consist of various compositions of properties that are found in their mechatronics, behavioural and cognitive architectures. Common properties of each architecture type serve as criteria for assessing the degree of intelligence of most embodied agent models. Although embodied intelligence has long been accepted for robotic agents, the literature is short on combined evaluations that discuss all properties of all architecture types in one framework. Here we provide a review of existing taxonomies for each type of architecture and attempt to combine them all in a single taxonomy for robotic agents.

SMC'98 Conference Proceedings. 1998 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.98CH36218), 2000

Here we describe an architecture for an intelligent distribution agent being designed for the Navy. This autonomous software agent will implement global workspace theory, a psychological theory of consciousness. As a result, it can be expected to react to novel and problematic situations in a more flexible, more human-like way than traditional AI systems. If successful, it will perform a function, namely billet assignment, heretofore reserved for humans. The architecture consists of a more abstract layer overlying a multi-agent system of small processors. The mechanisms implementing the architecture are quite varied and diverse, and are drawn mostly from the "new" AI. This paper is intended as a progress report.

1994

The current growth in networked information resources is radically changing the nature of computing. A decade ago, most machines had limited access to remote data and services. The rule today is networking and connectivity, even for the millions of personal computers ...

2009

This paper introduces a new model which is intended to combine the flexibility of a cognitive architecture and the power new technologies developed for using semantic knowledge in the WEB. The cognitive architecture (called TRIPLE) is hybrid integrate three modules which run in parallel: a reasoning, a connectionist and an emotion engines. The reasoning engine deal with the current task, processes the perceptual input to the system, and interacts with the memory of the architecture and the other two engines. The reasoning engine can augment by inference and consistency checks the representation of the task and the content of the working memory. The latter is determined by a spreading of activation mechanism performed by the connectionist engine which is also responsible for retrieval of knowledge from memory and mapping it to the task. This paper focuses on this latter module and presents its principles and implementation.

This report describes the High-bevel Symbolic Representation (HLSR) project for the U.S. Air Force PRDA 03-01-HE: Human Performance in Modeling and Simulation, Technical Area 2: Opposing Force Behaviors. This report summarizes the work done on Defense Modeling Simulation contract F33615-03-C-6343 to develop a high level symbolic representation (HLSR) for behavior modeling. This effort seeks to increase development efficiency and reuse in behavior modeling. The report describes the development of a high level language that abstracts the details of individual intelligent system architectures (ISA), allowing developers to focus their effort on tasks directly related to producing intelligent behavior. This language is designed to be complied into executable representations on multiple ISAs. This report targets two ISAs, Soar and ACT-R. These ISAs have a proven tract record of generating capable behavior models in many domains. There were three primary goals. First, the desire to constru...