An Interoperable Framework for Computational Models of Emotion

Computational models of emotion (CMEs) are software systems designed to emulate specific aspects of the human emotions process. The underlying components of CMEs interact with cognitive components of cognitive agent architectures to produce realistic behaviors in intelligent agents. However, in contemporary CMEs, the interaction between affective and cognitive components occurs in ad-hoc manner, which leads to difficulties when new affective or cognitive components should be added in the CME. This paper presents a framework that facilitates taking into account in CMEs the cognitive information generated by cognitive components implemented in cognitive agent architectures. The framework is designed to allow researchers define how cognitive information biases the internal workings of affective components. This framework is inspired in software interoperability practices to enable communication and interpretation of cognitive information and standardize the cognitive-affective communication process by ensuring semantic communication channels used to modulate affective mechanisms of CMEs

Analysis of Artificial Intellectual Agents for E-Learning Systems

The key terms and basic concepts of the agent are analyzed. The structured general classification of agents according to the representation of the model of the external environment, by the type of processing information and by the functions performed is given. The classification of artificial agents (intellectual, reflex, impulsive, trophic) also is s analyzed. The necessary conditions for the implementation of a certain behavior by the agent are given, as well as the scheme of functioning of the intelligent agent. The levels of knowledge that play a key role in the architecture of the agent are indicated. The functional diagram of a learning agent that works relatively independently, demonstrating flexible behavior. It is discussed that the functional scheme of the reactive agent determines the dependence on the environment. The properties of the intelligent agent are described in detail and the block diagram is indicated. Various variants of agent architectures, in particular neural network agent architectures, are considered. The organization of level interaction in the multilevel agent architecture is proposed. Considerable attention is paid to the Will-architecture and InteRRaP- architecture of agents. A multilevel architecture for an autonomous agent of a Turing machine is considered.

Towards Social Identity in Socio-Cognitive Agents

Current architectures for social agents are designed around some specific units of social behavior that address particular challenges, such as modeling beliefs and motivations, establishing social relationships, or understanding group memberships. Although their performance might be adequate for controlled environments, deploying these agents in the wild is difficult. Moreover, the increasing demand for autonomous agents capable of living alongside humans calls for the design of more robust social agents that can cope with diverse social situations. We believe that to design such agents, their sociality and cognition should be conceived as one. This includes creating mechanisms for constructing social reality as an interpretation of the physical world with social meanings and selective deployment of cognitive resources adequate to the situation. We identify several design principles that should be considered while designing agent architectures for socio-cognitive systems. Taking these remarks into account, we propose a socio-cognitive agent model based on the concept of cognitive social frames that allow the adaptation of an agent’s cognition based on its interpretation of its surroundings, its social context. Our approach supports an agent’s reasoning about other social actors and its relationship with them. Cognitive social frames can be built around social groups, and form the basis for social group dynamics mechanisms and construct of social identity.

Design Patterns for Social Intelligent Agent Architectures Implementation

Multi-agent systems (MAS) architectures are popular for building open, distributed, and evolving software required by today's business IT applications such as e-business systems, web services, or enterprise knowledge bases. Since the fundamental concepts of MAS are social and intentional rather than object, functional, or implementation-oriented, the design of MAS architectures can be eased by using social patterns. They are detailed agent-oriented design idioms to describe MAS architectures as composed of autonomous agents that interact and coordinate to achieve their intentions like actors in human organizations. This chapter presents social patterns and focuses on a framework aimed to gain insight into these patterns. The framework can be integrated into agent-oriented software engineering methodologies used to build MAS. The authors consider the broker social pattern to illustrate the framework. The mapping from system architectural design (through organizational architectural styles), to system detailed design (through social patterns), is overviewed with a data integration case study.

A survey on agent learning architecture that adopts internet of things and wireless sensor networks

Trustworthy and reliable applications built using intelligent software agents aim to provide improved performance using its characteristics. Agents introduced in various architectures represent its functionality as functional elements of the architecture and shows the interaction between other components present in the architecture. The Internet of things (IoT) reveals as a frequent technology that allows accessing the physical objects present in the world. IoT systems utilize wireless sensor network to transmit and receive data by establishing communication. Wireless Sensor Networks transmits digital signals to the cyber-world for analyzing and processing the information into useful data by either formulating or communicating with the intelligent and innovative system. While talking about IoT and WSN, agents introduced in such environments assist in making decisions quickly by perceiving the input from the environment. The number of agents needed for an application depends upon the complexity of the problem. Multi-Agent architectures discussed in the article describe their association, roles, functionality and interaction. This paper gives a detailed survey of various agent/multi-agent learning architectures introduced over IoT and WSN. Moreover, this survey with the performance and the SWOT analysis on the Agent-based learning architecture helps the reader and paves a way to pursue research on Agent-based architectural deployment over IoT and WSN paradigms.

Verifiable Autonomy and Responsible Robotics

The move towards greater autonomy presents challenges for software engineering. As we may be delegating greater responsibility to software systems and as these autonomous systems can make their own decisions and take their own actions, a step change in the way the systems are developed and verified is needed. This step involves moving from just considering what the system does, but also why it chooses to do it (since decision-making may be delegated). In this chapter, we provide an overview of our programme of work in this area: utilising hybrid agent architectures, exposing and verifying the reasons for decisions, and applying this to assessing a range of properties of autonomous systems.

BDI Agent Architectures: A Survey

The BDI model forms the basis of much of the research on symbolic models of agency and agent-oriented software engineering. While many variants of the basic BDI model have been proposed in the literature, there has been no systematic review of research on BDI agent architectures in over 10 years. In this paper, we survey the main approaches to each component of the BDI architecture, how these have been realised in agent programming languages, and discuss the trade-offs inherent in each approach.