Search

The Government of India offers various schemes for various classes of citizens. Most of the application forms of schemes to be filled are in English and it is observed that monolingual individuals find it difficult to access and fill the forms. This paper addresses the challenges faced by monolingual individuals in India, particularly the elderly, people with impairments, and those from marginalized communities. The proposed work is to create an interactive system called "Dhvani" voicebot, specifically designed for the Kannada language. It helps users in identifying suitable government schemes and fills forms in English. The proposed system is developed using the RASA chatbot framework and NLP techniques to comprehend user utterances. RNN and SVM algorithms are employed to ensure smooth conversation flow and interaction with the users. To enhance scheme suggestion accuracy, a knowledge graph is created, containing relevant data on government schemes. The intent classification model achieves an accuracy of 97%, indicating its ability to accurately understand user intentions. The integration of a knowledge graph improves the accuracy of scheme identification and suggestion to users. The system automates the process of filling out government scheme forms based on user inputs. Dhvani voicebot system presents a practical solution to address the challenges faced by monolingual individuals in accessing government schemes in India. The high accuracy of intent classification and the use of a knowledge graph contribute to the system's effectiveness. The study suggests that this system can be extended to other languages. An automated tool called "Dhvani" will solve the problem of aged, illiterate and physically challenged persons filling forms in post offices and banks. Most of the schemes, pension funds, cash withdrawal, cash deposit is through these organizations. So. the tool makes the process easier for the above mention persons without the help of others. An intent recognition and interactive tool developed in Kannada Language which is widely spoken in Karnataka, India. The digital resources available in Kannada Language is very sparce. Use of technology like interactive tool, Knowledge graph, RNN and SVM are used in the development of the tool. Government scheme recommendation interactively makes the users to choose the scheme faster in an interactive way. The form is filled automatically and can be edited to rectify mistakes. [ABSTRACT FROM AUTHOR]

The algorithmic turn has fundamentally transformed journalistic work. Academic interest in the implication of automated algorithms for journalism has grown hand-in-hand with their everyday use. This paper presents a literature review of peer-reviewed research reports (N = 62) on automated algorithms in the context of journalistic work. Our review focuses on the first decade (2010–2019) during which automated journalism gained traction. The study identifies the most prominent perspectives or themes that studies in automated journalism have explored and the future directions for research that researchers have proposed. Based on the analysis, the dominant themes that studies in automated journalism have covered include (1) testing and developing algorithmic tools, (2) developing practices and policies for journalistic work, (3) attitudes and technology acceptance, and (4) societal and macro-level discourses concerning AI and journalism. The new directions for research that studies on automated algorithms have recognized relate to (1) target groups and stakeholders—that is, who to study in the future; (2) emergent themes and phenomena—that is, what to study in the future; and (3) approaches and methodologies—that is, how to study these topics in the future. These findings help create a holistic picture of possible future directions for the field. [ABSTRACT FROM AUTHOR]

In this article, we explore the potential of enhancing academic prose and idea generation by fine-tuning a large language model (here, GPT-3) on one's own previously published writings: AUTOGEN ("AI Unique Tailored Output GENerator"). We develop, test, and describe three distinct AUTOGEN models trained on the prior scholarly output of three of the current authors (SBM, BDE, JS), with a fourth model trained on the combined works of all three. Our AUTOGEN models demonstrate greater variance in quality than the base GPT-3 model, with many outputs outperforming the base model in format, style, overall quality, and novel idea generation. As proof of principle, we present and discuss examples of AUTOGEN-written sections of existing and hypothetical research papers. We further discuss ethical opportunities, concerns, and open questions associated with personalized academic prose and idea generators. Ethical opportunities for personalized LLMs such as AUTOGEN include increased productivity, preservation of writing styles and cultural traditions, and aiding consensus building. However, ethical concerns arise due to the potential for personalized LLMs to reduce output diversity, violate privacy and intellectual property rights, and facilitate plagiarism or fraud. The use of coauthored or multiple-source trained models further complicates issues surrounding ownership and attribution. Open questions concern a potential credit-blame asymmetry for LLM outputs, the legitimacy of licensing agreements in authorship ascription, and the ethical implications of coauthorship attribution for data contributors. Ensuring the output is sufficiently distinct from the source material is crucial to maintaining ethical standards in academic writing. These opportunities, risks, and open issues highlight the intricate ethical landscape surrounding the use of personalized LLMs in academia. We also discuss open technical questions concerning the integration of AUTOGEN-style personalized LLMs with other LLMs, such as GPT-4, for iterative refinement and improvement of generated text. In conclusion, we argue that AUTOGEN-style personalized LLMs offer significant potential benefits in terms of both prose generation and, to a lesser extent, idea generation. If associated ethical issues are appropriately addressed, AUTOGEN alone or in combination with other LLMs can be seen as a potent form of academic enhancement. [ABSTRACT FROM AUTHOR]

This paper undertakes a comparative analysis of the famous eighteenth-century chess-playing automaton known as Mechanical Turk and the Amazon microwork platform of the same name. The original Mechanical Turk was a life-sized automaton made in 1770 and publicly exhibited until the mid-1800s, and which played games of chess with the audience. Its movements were fully mechanical, but even more remarkably, it was promoted as the world's first 'thinking machine,' deciding each move of the chess pieces for itself. From the outset, it was widely assumed that the Mechanical Turk was a hoax, and that a human must be hidden inside the machine, directing the game. But it was a clever hoax whose trick was never discovered, and widely admired as such. Amazon's Mechanical Turk job platform functions in a similarly open way, this paper argues, as a sort of open technological hoax. Mechanical Turk provides a source of human cognitive labour that can be used to invisibly operate digital systems and programs that are widely assumed to be fully automated. Artificial intelligence is a twenty-first century thinking machine, it requires a human brain to make it work. Amazon's Mechanical Turk is a marketplace in which companies can hire piecemeal cognitive labour to patch gaps and train programs to keep those systems functioning. Providing what Jeff Bezos has called 'artificial artificial intelligence,' Amazon's Mechanical Turk, like Kempelen's automaton, both draws attention to and obfuscates the limits of automation and artificial intelligence. Taking the two iterations of the Mechanical Turk as rich cultural figures of automation for their respective periods, this paper will argue that the open secret of their artificial artificial intelligence is itself a form of misdirection that hides other, more successfully guarded secrets: the true extent of that labour, and the conditions in which it is performed. [ABSTRACT FROM AUTHOR]

Given new technologies and algorithmic capabilities, human-agent teaming (HAT) is expected to dominate environments where complex problems are solved by heterogenous teams. In such teams, trust calibration is key; i.e. humans and agents working symbiotically, with humans trusting and relying on agents as appropriate. In this paper, we focus on understanding trust-calibration in HATs. We propose a theoretical framework of calibrated trust in HATs. Next, we provide a configurable testbed designed to investigate calibrated trust in HATs. To demonstrate the flexible testbed and our framework, we conduct a study investigating hypotheses about agent transparency and reliability. Results align with research to date, supporting the notion that transparency results in calibrated trust. Further, high transparency yielded more positive affect and lower workload than low transparency. We also found that increased agent reliability resulted in higher trust in the agent, as well as more positive valence. This suggests that participants experienced more engagement with the task when the agent was reliable and presumably trustworthy. We also build on our framework and testbed to outline a research agenda for the assessment of human trust dynamics in HATs and the development of subsequent real-time, intelligent adaptive systems. [ABSTRACT FROM AUTHOR]

This paper traces the infrastructural politics of automated music mastering to reveal how contemporary iterations of artificial intelligence (AI) shape cultural production. The paper examines the emergence of LANDR, an online platform that offers automated music mastering, built on top of supervised machine learning branded as artificial intelligence. Increasingly, machine learning will become an integral part of signal processing for sounds and images, shaping the way media cultures sound, look, and feel. While LANDR is a product of the so-called 'big bang' in machine learning, it could not exist without specific conditions: specific kinds of commensurable data, as well as specific aesthetic and industrial conditions. Mastering, in turn, has become an indispensable but understudied part of music circulation as an infrastructural practice. Here we analyze the intersecting histories of machine learning and mastering, as well as LANDR's failure at automating other domains of audio engineering. By doing so, we critique the discourse of AI's inevitability and show the ways in which machine learning must frame or reframe cultural and aesthetic practices in order to automate them, in service of digital distribution, recognition, and recommendation infrastructures. [ABSTRACT FROM AUTHOR]

The convergence of large datasets, increased computational power, and enhanced algorithm design has led to the increased success of machine learning (ML) and artificial intelligence (AI) across a wide variety of healthcare professions but which, so far, have eluded formal discussion in physiotherapy. This is a concern as we begin to see accelerating performance improvements in AI research in general, and specifically, an increase in competence within narrow domains of practice in clinical AI. In this paper we argue that the introduction of AI-based systems within the health sector is likely to have a significant influence on physiotherapy practice, leading to the automation of tasks that we might consider to be core to the discipline. We present examples of some of these AI-based systems in clinical practice, specifically video analysis, natural language processing (NLP), robotics, personalized healthcare, expert systems, and prediction algorithms. We address some of the key ethical implications of these emerging technologies, discuss the implications for physiotherapists, and explore how the resultant changes may challenge some long-held assumptions about the status of the profession in society. [ABSTRACT FROM AUTHOR]

Artificial Intelligence has the potential to be an important part of education governance. It is already being built into everything from business intelligence platforms to real-time online testing. In this paper, we aim to understand how AI becomes, and forms, a legitimate part of authority in contemporary education governance in what we call the automated education governance assemblage, that incorporates technology companies and AI-supported products used in education. We focus on EduTech Australia – an education technology trade show in Sydney – as a way to look at: (i) how the different aspects of automated governance are connected at EduTech, including the relations between different participants, companies and products; and (ii) how the automated governance assemblage works to legitimise and constitute EduTech as a policy space and site of new authorities in education governance. [ABSTRACT FROM AUTHOR]

In this paper, we replicated the method applied by Frey and Osborne to investigate the automation probability of jobs in Brazil, using data from Brazilian labor market administrative records between 1986 and 2017. We categorized each job listed on Brazil's Occupational Classification System into Job Zones based on its technical qualifications requirements and estimated the future demand for workers of each Job Zone from 2018 to 2046. To estimate the probability of automation for each occupation, we first collected the expert opinion of 69 specialists on artificial intelligence and then applied a Gaussian process using as input the text description of each occupation. The results showed that in 2017 55% of all formally employed workers in Brazil are in jobs with high or very high risk of automation, a value consistent with similar works found in the literature for other countries. The findings of this paper can aid policy makers to anticipate potential increased unemployment for occupations with high risk of automation, anticipate future transformations of the Brazilian labor market, and consequently support the planning of economic and social interventions. [ABSTRACT FROM AUTHOR]

New cognitive infrastructures are emerging as digital platforms and artificial intelligence enable new forms of automated thinking that shape human decision-making. This paper (a) offers a new theoretical perspective on automated thinking in education policy and (b) illustrates how automated thinking is emerging in one specific policy context. We report on a case study of a policy analysis unit ('The Centre') in an Australian state education department that has been implementing a BI strategy since 2013. The Centre is now focused on using BI to support complex decision making and improve learning outcomes, and their strategy describes this focus as becoming 'information centric'. The theoretical framework for our analysis draws on infrastructure studies and philosophy of technology, particularly Luciana Parisi's recent work on automated thinking. We analyse technical documentation and semi-structured interview data to describe the enactment of a BI strategy in The Centre, with a focus on how new approaches to data analytics are shaping decision-making. Our analysis shows that The Centre is developing a cognitive infrastructure that is already creating new conditions for education policy making, and we conclude with a call for research designs that enable pragmatic exploration of what these infrastructures can do. [ABSTRACT FROM AUTHOR]

RPA is used to complete repetitive tasks usually completed by humans. A robot is a software programed to mimic the operations of humans on computer systems. Robots are configured to accomplish these tasks using algorithms from the machine learning and artificial intelligence field. Using RPA leads to reduced errors in the processing of tasks and reduced completions times to carry out the tasks. This paper calculates the number of software licenses required to carry out thousands of transactions for financial services and proposes an assignment of the transactions to the robots. A two-phase linear integer optimization process is proposed. The first phase calculates the number of robots required to complete all of the tasks and the second phase assigns the tasks to the robots. Many different transaction types for financial services are to be completed, and each type has precise market operating hours, maximal delays in their treatment, different processing times and different volumes. The objective function minimizes the total number of robots, but also limits the number of robot startups and reconfigurations, since these operations take time to complete. The key behind the fast computing times to solve the problem is the formulation, where periods are defined at intersections of market operating hours. Preliminary tests are conducted on a real case provided by a bank in North America and a synthetic test case and show that the problem is solved quickly. [ABSTRACT FROM AUTHOR]

The paper dissects the intricacies of automated decision making (ADM) and urges for refining the current legal definition of artificial intelligence (AI) when pinpointing the role of algorithms in the advent of ubiquitous computing, data analytics and deep learning. Whilst coming up with a toolkit to measure algorithmic determination in automated/semi-automated tasks might be proven to be a tedious task for the legislator, our main aim here is to explain how a thorough understanding of the layers of ADM could be a first good step towards this direction: AI operates on a formula based on several degrees of automation employed in the interaction between the programmer, the user, and the algorithm. The paper offers a fresh look at AI, which exposes certain vulnerabilities in its current legal interpretation. To highlight this argument, analysis proceeds in two parts: Part 1 strives to provide a taxonomy of the various levels of automation that reflects distinct degrees of human-machine interaction. Part 2 further discusses the intricate nature of AI algorithms and considers how one can utilize observed patterns in acquired data. Finally, the paper explores the legal challenges that result from user empowerment and the requirement for data transparency. [ABSTRACT FROM AUTHOR]

As smart technologies such as artificial intelligence (AI), automation and Internet of Things (IoT) are increasingly embedded into commercial and government services, we are faced with new challenges in digital inclusion to ensure that existing inequalities are not reinforced and new gaps that are created can be addressed. Digital exclusion is often compounded by existing social disadvantage, and new systems run the risk of creating new barriers and harms. Adopting a case study approach, this paper examines the exclusionary practices embedded in the design and implementation of social welfare services in Australia. We examined Centrelink's automated Online Compliance Intervention system ('Robodebt') and the National Disability Insurance Agency's intelligent avatar interface 'Nadia'. The two cases show how the introduction of automated systems can reinforce the punitive policies of an existing service regime at the design stage and how innovative AI systems that have the potential to enhance user participation and inclusion can be hindered at implementation so that digital benefits are left unrealised. [ABSTRACT FROM AUTHOR]

This paper presents the formation control of a fleet of three small quadcopters in a motion capture environment. The dynamic model of a single quadcopter is derived for model predictive control (MPC) and then constraints are explained and expressed in an adequate manner to be included in the cost function for the optimization problem to be solved by the C/GMRES method. Two control architectures, centralized and decentralized, were implemented in the ROS framework and tested on the CrazyFlie quadcopter. First performances are assessed for a static reference, the formation regulation problem, then for a dynamic reference, the formation tracking one. Finally, computational cost of the MPC controllers is evaluated. [ABSTRACT FROM AUTHOR]

Advancements in autonomy are beginning to allow humans to partner with machines in order to accomplish work tasks in various settings. As human–agent teaming (HAT) becomes more prevalent as a research topic, the need to understand humans’ psychological perceptions of the machine partner is increasingly important, especially in terms of its perceived role, which may ultimately impact trust and team effectiveness. Specifically, it remains unclear how humans perceive intelligent agents and how consistent these perceptions are with existing taxonomies found in the psychology of teams. The present paper presents a definition of the construct ofautonomous agent teammate-likeness(AAT) and a conceptual model of its components, reviews related concepts and germane research and proffers a number of propositions to guide future research. The goal is to contribute to the nascent literature on HAT by establishing a theoretical foundation for the AAT construct, upon which researchers can advance research on HAT. [ABSTRACT FROM PUBLISHER]

This paper addresses autonomous intelligent navigation of mobile robotic platforms based on the recently reported algorithms of language-measure-theoretic optimal control. Real-time sensor data and model-based information on the robot's motion dynamics are fused to construct a probabilistic finite state automaton model that dynamically computes a time-dependent discrete-event supervisory control policy. The paper also addresses detection and avoidance of livelocks that might occur during execution of the robot navigation algorithm. Performance and robustness of autonomous intelligent navigation under the proposed algorithm have been experimentally validated on Segway RMP robotic platforms in a laboratory environment. [ABSTRACT FROM AUTHOR]

The paper introduces some issues and related solutions adopted in order to realize the MMASS platform. This is a framework to specify and execute simulation applications based on the multi-layered multi-agent situated system model (MMASS). MMASS is a model for multi-agent systems (MAS) situated in an environment whose structure is explicitly defined. The behavior and interaction models of MMASS agents are strongly related to the spatial structure of their environment. The MMASS model is the result of a long-term research that has its roots and motivations on application domains and problems that require spatial features to be considered. Our experiences with these problems have concerned the design of domain models and their implementations, according to the MAS approach for simulation purposes. This activity has revealed that currently available tools do not support the management of spatial features of agent environment and interaction mechanisms defined by the MMASS model and thus they are not suitable for our purposes. The paper focuses on the MMASS platform that aims to support the specification and development of applications (mainly, simulations) based on MMASS. Design issues and related solutions that have been adopted in order to manage those aspects that characterize the MMASS model will be shown. After a description of the conceptual model that underlies the MMASS platform and its general architecture, we will overview how the platform supports the specification of agent structured environment, behavior and interaction, and how it supports the execution of agent actions and interactions. Then we will describe issues and adopted solutions (both algorithmic and implementative ones) to manage at-a-distance interaction among MMASS agents. [ABSTRACT FROM AUTHOR]

In this paper, we discuss the limitations of current evolutionary robotics models and we propose a new framework that might solve some of these problems and lead to art open-ended evolutionary process in hardware. More specifically, the paper describes a novel approach where the usual concepts of population, generations and fitness tire made implicit in the system. Individuals co-evolve embedded in their environment. Exploiting the sell-assembling capabilities of the (simulated) robots, the genotype of a successful individual can spread in the population. In this way, interesting behaviours emerge spontaneously, resulting in chasing and evading other individuals, collective obstacle avoidance and co-ordinated motion of self-assembled structures. [ABSTRACT FROM AUTHOR]

In this paper, we focus on the problem of having a multitude of very simple mobile robots self-organize their relative positions so as to obtain a variety of spatial configurations. The problem has a variety of applications in mobile robotics, modular robots, sensor networks, and computational self-assembly. The approach we investigate in this paper attempts at minimizing the local capability of robots and at verifying how and to what extent a variety of global shapes can be obtained by exploiting simple self-organizing algorithms and emergent behaviors. Several experiments are reported showing the effectiveness of the approach. [ABSTRACT FROM AUTHOR]

This paper describes an automated process for designing mechanical systems based on the adaptive multi-agent system(AMAS)theory. At the beginning of the design process, the designer adds the elements of his problem: goal, envelope, constraints, known mechanical components, etc. During the solving process, mechanical components(previously“agentified”)organize themselves in order to find a solution, by modifying their parameters, by creating new mechanical components fitting in with the needs, or by modifying the system topology. While this paper presents an overview of the theoretical basis of the AMAS theory, it primarily focuses on the method for developing the mechanical synthesis solver and the results from this prototype. [ABSTRACT FROM AUTHOR]

In autonomous agent systems, internal memory can be an important element to overcome the limitations of purely reactive agent behaviour. This paper presents an analysis of memory requirements for T-maze tasks well known as the road sign problem. In these tasks, a robot agent should make a decision about turning left or right at the T-junction in the approach corridor, depending on a history of perceptions. The robot agent in simulation can sense the light intensity influenced by light lamps placed on the bank of the wall. We apply the evolutionary multiobjective optimization approach to finite state controllers with two objectives: behaviour performance and memory size. Then the internal memory is quantified by counting internal states needed for the T- maze tasks in noisy environments. In particular, we focused on the influence of noise on internal memory and behaviour performance, and it is shown that state machines with variable thresholds can improve the performance with a hysteresis effect to filter out noise. This paper also provides an analysis of noise effect on perceptions and its relevance on performance degradation in state machines. [ABSTRACT FROM AUTHOR]

This paper presents an inquiry concerning the feasibility of using existing methods from the field of Music Information Retrieval (MIR) for automated annotation of acousmatic music. Thorough discussion and appraisal of the meaning and role of annotation in this context leads to our conclusion that: (i) full automation is not possible due to the lack of a ‘ground truth’ and the absence of semantic comprehension on the side of the computer, (ii) MIR can nevertheless play a valuable role by providing human annotators with tools for interactive annotation. We present two possible approaches to interactive annotation applied to compositions of acousmatic music, namely John Chowning's Turenas and Denis Smalley's Wind Chimes. We also discuss the potential impact of such semi-automatic annotation on the theoretical consideration and practice of acousmatic music. [ABSTRACT FROM AUTHOR]

The expression "artificial intelligence" (AI) was introduced by John McCarthy, and the official birth of AI is unanimously considered to be the 1956 Dartmouth Conference. Thus, AI turned fifty in 2006. How did AI begin? Several differently motivated analyses have been proposed as to its origins. In this paper a brief look at those that might be considered steps towards Dartmouth is attempted, with the aim of showing how a number of research topics and controversies that marked the short history of AI were touched on, or fairly well stated, during the year immediately preceding Dartmouth. The framework within which those steps were taken was the development of digital computers. Earlier computer applications in areas such as complex decision making and management, at that time dealt with by operations research techniques, were important in this story. The time was ripe for AI's intriguingly tumultuous development, marked as it has been by hopes and defeats, successes and difficulties. [ABSTRACT FROM AUTHOR]

Social norms are cultural phenomena that naturally emerge in human societies and help to prescribe and proscribe normative patterns of behaviour. In recent times, the discipline of multi-agent systems has been used to model social norms in an artificial society of agents. In this paper we review norms in multi-agent systems and then explore a series of norms in a simulated urban traffic setting. Using game-theoretic concepts we define and offer an account of norm stability. Particularly in small groups, a relatively small number of individuals with cooperative attitude are needed for the norm of cooperation to evolve and be stable. In contrast, in larger populations, a larger proportion of cooperating individuals are required to achieve stability. [ABSTRACT FROM AUTHOR]

What dynamics do simple recurrent networks (SRNs) develop to represent stack-like and queue-like memories? SRNs have been widely used as models in cognitive science. However, they are interesting in their own right as non-symbolic computing devices from the viewpoints of analogue computing and dynamical systems theory. In this paper, SRNs are trained on two prototypical formal languages with recursive structures that need stack-like or queue-like memories for processing, respectively. The evolved dynamics are analysed, then interpreted in terms of simple dynamical systems, and the different ease with which SRNs aquire them is related to the properties of these simple dynamical systems. Within the dynamical systems framework, it is concluded that the stack-like language is simpler than the queue-like language, without making use of arguments from symbolic computation theory. [ABSTRACT FROM AUTHOR]

The evolution of powerful portable networked devices allows nomadic users seamless access to business and leisure information any time and any where. This drives service infrastructures to similarly evolve to meet new service management challenges in order to adapt information delivery to a richer spatial, temporal, and user context whilst balancing the concerns of the different stakeholders. This paper presents the use of a multi-agent system service infrastructure and associated privacy model to maintain the balance between the service provider's need to maximize its knowledge of the user context against the need for the user's privacy to be protected. [ABSTRACT FROM AUTHOR]

In this paper we present the agent architecture development environment (ADE), intended for the design, implementation, and testing of distributed robotic agent architectures. ADE is unique among robotic architecture development environments in that it is based on a universal agent architecture framework called APOC, which allows it to implement architectures in any design methodology, and in that it uses an underlying multi-agent system to allow for the the distribution of architectural components over multiple host computers. After a short exposition of the theory behind ADE, we present the multi-agent system setup and give an example of using ADE in a multi-robot setting. A general discussion then highlights some of the novel features of ADE and illustrates how ADE can be used for designing, implementing, testing, and running agent architectures. [ABSTRACT FROM AUTHOR]

Many researchers in embodied cognitive science and artificial intelligence, and evolutionary robotics in particular, emphasize the interaction or brain, body and environment as crucial to the emergence of intelligent. adaptive behaviour. Accordingly. the interaction between agent and environment. as well as the co-adaptation of artificial brains and bodies, has been the focus of much research in evolutionary robotics. Hence, there are plenty of studies of robotic agents/species adapting to a given environment. Many animals, on the other hand, in particular humans, to some extent can choose to adapt the environment to their own needs instead of adapting (onIy) themselves. That alternative has been studied relatively little in robot experiments. This paper, therefore, presents some simple initial simulation experiments, in a delayed response task setting, that illustrate how the evolution of environment adaptation can sent to provide cognitive scaffolding that reduces the requirements for individual agents. Furthermore, theoretical implications, open questions and future research directions for evolutionary robotics are discussed. [ABSTRACT FROM AUTHOR]

Modeling the environment and agent-environment relationships is not well explored in multi-agent systems, in particular not for software multi-agent systems. This paper aims to contribute with a generic model for active perception in situated multi-agent systems. Active perception enables an agent to direct its perception at the most relevant aspects in the environment, according to its current task. The model decomposes perception into three functionalities: sensing, interpreting, and filtering.The agent first senses its neighborhood through a set of selected foci, resulting in a representation. A set of perceptual laws enforces domain specific constraints on sensing. Next, the agent interprets the representation by means of descriptions, resulting in a percept. Percepts are expressions that can be understood by the internal machinery of the agent. Finally, the percept is filtered by a set of selected filters, restricting the perceived data according to specific context relevant selection criteria. [ABSTRACT FROM AUTHOR]

Microscopic analysis is a standard approach in the study of robot behaviour. Typically, the approach comprises the analysis of a single (or sometimes a few) robot-environment system(s) to reveal specific properties of robot behaviour. In contrast to microscopic analysis, macroscopic analysis focuses on averaged properties of systems. The advantage is that such a property is easier to generalize so that it can be established to what extent the property is universal. This paper investigates whether a macroscopic analysis can reveal a universal property of adaptive behaviour in a robot model of foraging behaviour. Our analysis reveals that the step lengths of the most successful robots are distributed according to a Lévy-flight distribution. From studies on a variety of natural species, it is known that such a distribution constitutes a universal property of foraging behaviour. Thereafter, we discuss an example of how macroscopic analysis can be applied to existing research in evolutionary robotics, and relate the macroscopic and microscopic analyses of foraging behaviour to the framework of scientific research described by Cohen (1995, Empirical Methods for Artificial Intelligence (Cambridge MA: MIT Press)). We conclude that macroscopic analysis may predict universal properties of adaptive behaviour and that it may complement microscopic analysis in the study of adaptive behaviour. [ABSTRACT FROM AUTHOR]

When developing teams of humanoids — rather than an individual humanoid — there are a number of issues that become important to consider, including robustness, scalability, versatility, and also development and production costs. Therefore, we used a modern approach to AI that puts emphasis on the balance between control, electronic hardware, material, sensory system and energy in order to develop the team of Viki humanoid robots. In contrast to the top-down approach of equipping a humanoid with as many sensors, motors, power, etc., as possible, we developed a bottom-up approach to the construction of humanoids, regarding both hardware and software (modular behaviorbased control). The approach is shown with the development of the Viki humanoid team that won the RoboCup Humanoids Free Style World Championship 2002. In this paper, we focus on the main result of the behavior-based architecture with many layers of behaviors at different levels, which make it easy for both engineers to design new behaviors and for end-users to develop humanoid behaviors at different levels of complexity, dependent on the competencies of the individual end-user. With this architecture, it becomes possible to develop simple user interfaces with a user-guided implementation of our modular behavior-based approach, in order to allow any user to design performances with the humanoid robots. [ABSTRACT FROM AUTHOR]

This paper presents the design and evaluation of a manufacturing route planner for flexible manufacturing systems. The aim of the planner is to find the optimal manufacturing routes for jobs using well-designed cost functions. This route planner, which is based on heuristic [ A *] strategy, has been designed to work under dynamic arrival pattern environment. The proposed algorithm consists of two level; the cell level, which controls the jobs flow between cells, and the machine level which controls the jobs flow between the machines inside the cells selected at first level. The solutions of the route planner are contained in a dynamic knowledge base that passes information to the sequencing and monitoring stage. The suggested model has also the capability to detect and response with suitable alternation for the damaged elements in the manufacturing system. To improve the performance of the planner, the design is organized into distributed programming media using the concurrent features of the modula-2 programming language. Finally, two case studies are considered to illustrate the functionality of the proposed algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we consider an actual industrial warehouse order picking problem where goods are stored at multiple locations and the pick location of goods can be selected dynamically in near real time. We solve the problem using an intelligent agent-based model. The modeling Framework is between the two extremes of hierarchical and heterarchical frameworks. It recognizes that horizontal as well as vertical decisions are made between various levels of controllers and that these have to be captured explicitly in the model. Entities (goods or parts) and resources (storage areas and order pickers) are modeled as intelligent agents that function in a co-operative manner so as to accomplish individual as well as system-wide goals. Scheduling and other decisions are taken by these agents in a dynamic real-time fashion basal on conditions that exist at the time these decisions are made. To overcome structural rigidity and lack of flexibility, a negotiation mechanism for real time ask allocation is used. [ABSTRACT FROM AUTHOR]

In this paper an analytical procedure implemented to structure the knowledge required by an expert system applied for controlling a chemical process is described. The analytical approach is based on L ifecycle of Systems and on Task Model approaches. It was validated with an experimental procedure related to the determination of the critical point of pure fluids (CO[sub 2], N[sub 2]O, SF[sub 6]) and mixtures of low concentration (CO[sub 2]-limonene). For the implementation of the control system an expert system shell (CHRONOS™) was used. [ABSTRACT FROM AUTHOR]