The Integration and Control of Behaviour

Clues to the way behaviour is integrated and controlled in the human mind have emerged from cognitive psychology and neuroscience. The picture that is emerging mirrors solutions (driven primarily by engineering concerns) to similar problems in the rather different domains of mobile robotics and intelligent agents in artificial intelligence (AI). This chapter looks in detail at the relationship between a psychological theory of willed and automatic control of behaviour, the Norman and Shallice framework, and three types of engineering-based theory in AI. As well as being a promising basis for a large-scale model of cognition, the Norman and Shallice framework presents an interesting example both of apparent theoretical convergence between AI and empirical psychology, and of the way in which theoretical work in both fields can benefit from interaction between them.

Intelligent mobile agents are sent between routers to carry out a set of required tasks. Mobile agents can carry algorithms and information to various routers they visit. Critical information that all mobile agents should carry includes: timer, navigation algorithm, network information, stability of the network, and load traffic of the network. By clustering intelligent mobile agents, It is possible to minimize the cost of time for each agent to travel to its required routers and the number of intelligent mobile agents. Each router has certain capability and hence an only process certain requests of each intelligent agent. The problem is to group the intelligent mobile agent into a number of clusters such 1h.t that each cluster has similarity in responding to a group of routers. This can significantly decrease the demand imposed upon a network for a set of required task be he performed. In this paper, we present the problem of multiple criteria clustering of intelligent mobile agents where the decision is to cluster mobile agents such that a group of similar intelligent mobile agents will visit a group of similar routers. This grouping will decrease the total number of visits and the total number of agents, decreasing demand on the network.

So far, agent research can be broadly categorized into two areas: intelligent agents and mobile agents. Intelligent agent research has grown from a background of AI, humancomputer interaction, and autonomous systems, while mobile agents research stems from the areas of distributed computing and networked systems. Intelligent agents research addresses intelligent control, planning, domain modeling, user modeling, perception, interagent cooperation, and adaptivity. Intelligent agents can take on many different roles, ranging from representing a human user’s interests to actively collaborating with users and each other. Intelligent agents are being designed to tackle nearly every tedious, non-creative activity that people perform (e.g., Web searches), as well as many more abstract functions (e.g., entertainment). Major issues in intelligent agents research include knowledge representation and communication, architectures for intelligent autonomy, mixed-initiative behavior, collaboration techniques, and learning. Mobile agents address physical communication, agent mobility, tracking and locating mobile agents, security and reliability of agents, etc. Mobile agents are suited for optimizing communication ‐ moving the action towards the source of data ‐ and thereby avoiding the need for huge data transfers. This is beneficial in data-intensive applications such as data mining. Mobility can also improve reliability in certain cases. In the field of electronic commerce, mobile agents naturally reflect the characteristics of buyer and the seller, effectively playing the roles of traveling salesman or broker. They can also be applied for easier software customization, software distribution, and to represent disconnected users. The main issues are in security, providing the infrastructure on the Web (naming, locating, controlling, and messaging) and improving survivability. The biggest challenge for all new agent technology is developing successful applications. For this minitrack, we selected two characteristics of agents that can span the diverging areas of intelligent and mobile agents: mobility and communication. The accepted papers reflect the research issues described in this summary. The minitrack is organized in three sessions. The first session explores the coordination of multiple intelligent agents using dramatically different communication schemes. Andersson and Ygge present an efficient market-based approach to managing very large scale groups of agents engaged in coordination via producer/ consumer transactions. Their CoTree algorithm allows largescale markets to operate efficiently with sparse communication, rapid updates as agent preferences change, and minimal data caching requirements on massively distributed systems. In contrast, Nelson, Penner, and Soken discuss the Interaction Society model of coordination, in which relatively small-scale groups of knowledge-intensive agents collaborate using complex communication about discrete tasking and information requirements. Their model for inter-agent communication and interactions with human users is being applied to build a multi-agent advising system for coordinating military Search and Rescue operations. Finally, Barbuceanu discusses the role of obligations and interdictions in the context of coarse-grain agent interactions. By explicitly capturing social obligations and the costs associated with violating those constraints, he shows how rational agents can achieve coordinated behavior without necessarily sharing goals. The second session addresses Java-based mobile agent systems. The paper by Covaci, Busse and Zhang, describes how a mobile agent system can be used for network management. Their framework is based on existing solutions such as TMN, and can be extended to SNMP.

Artificial intelligence and cognitive science must look at the world of industrial-process control to find the technological reifications of the concept of mind.

Recently, smart robots and smart systems have acquired extensive research. Combination of control engineering along with automation engineering and artificial intelligence has given birth to the next generation robotic systems and modern control engineering. Intelligent agent which is considered as the basic building block is discussed in this paper then based on the theory of artificial intelligence, image processing and control system a robotic model which is called a Combi-Forklift robot is proposed. Different kind of machines have been researched which are named as robot. Some of them contain complex mechanical structure, others contain of complex electrical circuits and some of them are designed based on extensive work of programming. Some robots recognize voice, while other recognizes the image. Some are good in audio communication and some are combined with all such properties in a human like body. Moreover, robots have been researched and designed based on application, model or domain. In this paper we have proposed a combi-forklift robot by implementing a sensor network as an agent of artificial intelligence. The sensor network acts as the intelligent interface between the human being and the robot in a way that it observes the motion and gesture of human being and communicates the commands to our designed robot in order to perform application based activities. Since the indoor sensor network based system acts as intelligent agent, therefore the outdoor combi-forklift robot acts as the effector.

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Urgency of the research of artificial intelligence research in a digital society presents numerous causal changes, among them: space research, biotechnology, nanotechnology, digital technology, etc. The implications for human beings and human civilisation are significant as they affect a number of already existing contemporary issues such as economics, politics, ethics, law, psychology, conflictology, sociology, and ecology. Anthropology has to return to the existence of human beings in a society of artificial intelligence, consciousness and even the physical body. The study conceptualises artificial intelligence as a factor of ecosystem growth in the context of technological, ontological, and anthropological dimensions. The research objectives: 1) to theorise artificial intelligence as a potential and resource for use in all sectors of the socioeconomic sphere in the context of technological dimensions; 2) to clarify the convergence of artificial intelligence and its main components in the context of ontological dimensions; 3) to reveal the role of artificial intelligence in enhancing the digitalisation of society and humans in the context of anthropological dimensions. In the research we are guided by the works of scholars who have studied and are studying the problems of artificial intelligence as a complex social, economic, and cultural phenomenon: R. Andriukaitienė, N. Boström, A. Vance, P. Diamandis, S. Kotler, P. Dickson, L. Kai-Fu, and K. Kell, K. Kell, and others. Research methodology. The theory of artificial intelligence is a complex multidisciplinary science, which is located at the junction of economics, sociology, computer science, mathematics, psychology. Summarizing the processes of interdisciplinary interaction of artificial intelligence, we note that its study requires an interdisciplinary approach based on the synthesis of scientific knowledge, as revealing the content and principles of functioning of a complex invariant system of artificial intelligence. To analyze the modern model of artificial intelligence, it is necessary to apply a systematic institutional-evolutionary approach. By analysing different methodological approaches, it is necessary to acknowledge the presence of an anthropogenic component, as the methodology of scientific research is based on the method of information-anthropogenic analysis based on the fundamental position that no artificial intelligence processes are possible without information/data as an object and human as a subject of cognition. In addition, the following methods were used: induction, deduction, sublimation, synergetics, evolutionary historicism, modelling, and forecasting. The result of the research. Artificial Intelligence (AI) is a broad term used to define technologies/ engineering systems that emulate human intelligence, linking to neurophysiology, robotics, psychology (pattern recognition, modelling of psychological processes), transhumanism, cybernetics (computing power to find patterns in large data sets). Artificial Intelligence has been defined as a software-engineered system that is capable of influencing the environment with varying degrees of autonomy, producing results (predictions, recommendations, decisions) for a specific set of goals for the digitalisation of society. On the other hand, artificial intelligence causes many problems that are seen as cyber/virtual addiction, loss of reality, increased surveillance, loss of protocol control, privacy issues, cybersecurity, etc. The convergence of large data with artificial intelligence has become the single most important development shaping the future of social institutions in the digital society and the prospects for realising potential opportunities. Thanks to the latest digital technologies, advances in software engineering, and evolving artificial intelligence, there are both new perspectives for solving the most complex problems of modern civilization and new challenges in the technological, ontological, and anthropological dimensions.

Artificial intelligence and robots may progressively take a more and more prominent place in our daily environment. Interestingly, in the study of how humans perceive these artificial entities, science has mainly taken an anthropocentric perspective (i.e., how distant from humans are these agents). Considering people’s fears and expectations from robots and artificial intelligence, they tend to be simultaneously afraid and allured to them, much as they would be to the conceptualisations related to the divine entities (e.g., gods). In two experiments, we investigated the proximity of representation between artificial entities (i.e., artificial intelligence and robots), divine entities and natural entities (i.e., humans and other animals) at both an explicit (Study 1) and an implicit level (Study 2). In the first study, participants evaluated these entities explicitly on positive and negative attitudes. Hierarchical clustering analysis showed that participants’ representation of artificial intelligence, robots and divine entities were similar, while the representation of humans tended to be associated with that of animals. In the second study, participants carried out a word/non-word decision task including religious semantic-related words and neutral words after the presentation of a masked prime referring to divine entities, artificial entities and natural entities (or a control prime). Results showed that after divine and artificial entity primes, participants were faster to identify religious words as words compared to neutral words arguing for a semantic activation. We conclude that people make sense of the new entities by relying on already familiar entities and in the case of artificial intelligence and robots, people appear to draw parallels to divine entities.

The thriving contemporary form of artificial intelligence (AI) called machine learning is often represented sensationally in popular media as a semi-mystical technology. Machine learning systems are frequently ascribed anthropomorphic capacities for learning, emoting and reasoning which, it is suggested, might lead to the alleviation of humanity’s woes. One critical reaction to such sensational proclamations has been to focus on the mundane reality of contemporary machine learning as mere inductive prediction based on statistical generalizations, albeit with surprisingly powerful abilities (Pasquinelli 2017). While the deflationist reaction is a necessary reply to sensationalist agitation, adequate comprehension of modern AI cannot be achieved while neglecting its material and social context. One does not have to subscribe wholeheartedly to the social construction of technology thesis1 to allow that the development and evolution of technologies are influenced by social factors. For AI, the most important aspect of the current social context is arguably capital, which increasingly dominates AI research and production. One former computer science professor describes a “giant sucking sound of [AI] academics going into industry” (Metz 2017). This paper introduces capital’s theory of AI as utility and initiates a discussion on its social consequences. First, I discuss utilities and their infrastructures and introduce a few critical thoughts on the topic. Second, I situate modern AI by way of a brief history. Third, I detail capital’s view of AI as a utility and the technical details underpinning it. Fourth, I sketch how AI as a utility frames a social problematic beyond the important issues of algorithmic bias and the automation of work. I do so by extrapolating from one consequence of AI as a utility which multiple capitalist firms predict: the curation of human subjectivities.

Artificial intelligence (AI) is widely used in medicine. AI may provide low-cost solutions to health problems and is especially important for developing countries. Health-care professionals will play an important role in providing data for educating AI systems and validating these through clinical trials. AI may necessitate changes in the different roles of a physician and possibly other professionals. Intelligent tutoring systems can support student learning by providing individualized feedback and creating personalized learning pathways. Role-plays with an intelligent active agent can enhance students' interaction with computers and activate their sense of responsibility. AI can support personalized learning by intelligent agents, autonomous scoring, and chatbots. AI has an important role to play in supporting simulations, serious games, and the gamification of learning. Learning analytics and educational data mining are two other important applications. Personalized prediction is also an important benefit. AI will supplement the work of educators and can reduce curricular overload by migrating some knowledge to AI algorithms. Routine tasks and responses to routine queries of learners can be provided by AI. AI can support continuing professional education by incorporating longitudinal and innovative formative assessment methods that can help identify knowledge and skill gaps and support learning. The use of AI in curriculum review and assessment has been limited. Data integrity and privacy are important issues to consider. Unconscious bias in the data used to educate AI systems is also possible. Most of the literature is from developed countries and among medical students and residents.

Artificial intelligence of large ensembles of intelligent agents in terms of computing power surpasses human intelligence. He is capable of artificial thinking and understanding. Giant ensembles of intellectual agents with artificial consciousness and intelligence are able, for the results set by the person necessary for him, to find solutions for their obtaining on the basis of professional competence and experience accumulation. The professional competence of artificial intelligence is the ability to use technologies, including computer vision, natural language processing, speech recognition and synthesis, intelligent decision support, as well as the use of synergistic methods, functional self-organizing methods and utility and preference criteria. For artificial intelligence, the functional organization of the system is important. The functionalism of artificial intelligence does not depend on its carrier, allows many methods of its functional implementation, as well as to form the completeness of its functions. Giant ensembles of intellectual agents with a full set of functions gradually and flexibly form events into solutions or rational behavior to obtain a given necessary result. Intelligent artificial intelligence has psychological, analytical, research, language, professional and behavioral hybrid competencies. Each competence is exercised by an intelligent agent with a competent functional professional manner. Intelligent agents form an ensemble with intelligent ethical artificial intelligence. Functional harmonious self-organization of the interaction of intellectual agents in different environments is carried out on the basis of data of a specific environment obtained by analytical competent intellectual agents. For each set of functions and smart hybrid competencies of an intelligent ensemble, there is a critical importance for the number of its intellectual agents capable of synergistic self-organization of interaction.

In this chapter we describe the work done in the EU project ASPIC: Argumentation Service Platform with Integrated Components. The main goals of which were 1) to develop a solid theoretical ground for the Argumentation Theory in Artificial Intelligence; 2) based on the theoretical work, develop practical-software components that embody standards for the argumentationbased technology (inference, decision-making, dialogue and learning); and 3) In order to test these components develop two large scale demonstrators. One of these large scale demonstrator is motivated on a medical problem. In particular, how to increase human organ availability for transplantation. It is this medical large scale demonstrator that we focus on in this chapter.

This paper presents IMAP, a hybrid agent platform composed of several cooperating intelligent agents and mobile agents. IMAP is implemented in Java and Prolog. Java is used to implement the framework of the system, and in particular for supporting the communication between agents and mobility of agent, while Prolog is used to implement both adduction and derivation mechanisms. IMAP intends to independently employ the underlying derivation/adduction and mobility mechanism. In IMAP, intelligent agent and mobile agent can not only fully exploit individual virtue, but also cooperate to perform a task under a uniform platform. Intelligent agents in IMAP are equipped with hypothetical reasoning capabilities, performed by means of adduction: if the knowledge available to an agent is insufficient to solve a query, the agent could adduce new hypotheses. Each intelligent agent can accept queries from mobile agents by means of the interface module, each query is passed to the reasoning module of intelligent agent which performs a derivation and adduction in order to get an answer for mobile agent. IMAP also provides mobile agents a flexible and efficient coordination mechanism and a reliable migration mechanism, and supports persistence of agent state and agent security. Mobile agent's coordination mechanism exploits the advantages of the XML language and Linda-like coordination. This programmable Linda-like coordination mechanism suits the mobility and openness of the Internet application, XML standard for Internet data representation may guarantee a high-degree of interoperability between heterogeneous environments. The design and implementation key technologies of IMAP are described in this paper. An Internet based auction application example shows the suitability and the effectiveness of the IMAP, and its performance evaluation is also made. Finally, some conclusions and remarks are given.

Previous research on the representation of artificial intelligence (AI) in science fiction (SF) film has largely focused on the Global North. Little is known about how filmmakers from the Souths envision and portray AI and how their creative processes and outputs relate to specific conditions of film production, socio-political realities, and global power imbalances, as well as personal experiences and attitudes toward AI. This paper aims to address this gap by presenting the findings of an exploratory interview study with Latin American filmmakers who have dealt with AI in their films. The qualitative text analysis of 10 in-depth, semi-structured interviews with 11 participants from 6 countries reveals that their cinematic visions of AI differ significantly from those prevalent in Western SF: The Latin American filmmakers predominantly associate AI with social control, surveillance, labor exploitation, data colonialism, and the abuse of power by (authoritarian) governments or powerful tech corporations—issues that decolonial scholars have emphasized in their critiques of AI. However, the participants’ visions are not primarily inspired by these academic debates but by their lived experiences. The filmmakers’ strong engagement with everyday life and socio-political conditions, rather than distant or unattainable technological scenarios, can, thus, be considered a distinctive feature of the Latin American approach. This is further reflected in their use of particular esthetic concepts and strategies, such as retrofuturism, which enable them to navigate budgetary constraints and other structural inequalities that limit their visibility and their potential to actively shape technological visions and futures through film.

This study aims to develop AI․SW liberal arts subjects for university learners based on the analysis of learners' needs for artificial intelligence and software education. To analyze learners' educational needs, a survey was conducted on learners at A University. Based on the analysis, the AI․SW liberal arts subjects was designed to combine learning theory of artificial intelligence and Python practice. The theory of artificial intelligence focuses on the development process and service cases to increase the interest of learners. It helps learners explore ways to use artificial intelligence in their field. And Python practice is to create results by modifying the source code to reduce the burden of beginners. In the long term, it helps learners integrate artificial intelligence into their majors. This study presents basic research for a liberal arts curriculum that creates talents who can converge artificial intelligence in each academic field.