AGENT COGNITION PROCESSES

Let us start with the popular agent-computing model the Beliefs, Desire, and Intentions, henceforth abbreviated as the BID model (Brazier-Truer et al., 1995). BID is a generic agent-computing model specified within the declarative compositional modeling framework for multiagent systems, DESIRE. The model, a refinement of a generic agent model, explicitly specifies motivational attitudes and the static and dynamic relations between motivational attitudes. Desires, goals, intentions, commitments, plans, and their relations are modeled. Different notions of strong and weak agency are presented at (Wooldridge and Jennings, 1995). To apply agent computing with intelligent multimedia some specific roles and models have to be presented for agents. The BID model has emerged for a

“rational agent”: a rational agent described using cognitive notions such as beliefs, desires and intentions. Beliefs, intentions, and commitments play a crucial role in determining how rational agents will act. Beliefs, capabilities, choices, and commitments are the parameters making component agents specific. A generic BID agent model in the multiagent framework DESIRE is presented towards a specific agent model. The main emphasis is on static and dynamic relations between mental attitudes, which are of importance for cooperative agents. DESIRE is the framework for design, and the specification of interacting reasoning components is a framework for modeling, specifying and implementing multiagent systems (Brazier, Dunin-Keplicz, Jennings, and Treur, 1995, 1996; Dunin-Keplicz and Treur, 1995). Within the framework, complex processes are designed as compositional architectures consisting of interacting task- based hierarchically structured components. The interaction between components, and between components and the external world is explicitly specified. Components can be primitive reasoning components using a knowledge base, but may also be subsystems that are capable of performing tasks using methods as diverse as decision theory, neural networks, and genetic algorithms. As the framework inherently supports interaction between components, multiagent systems are naturally specified in DESIRE by modeling agents as components. The specification is sufficient to generate an implementation. Specific techniques for such claims might be further supported at (Nourani 1993a, 1999a). A generic classification of mental attitudes is presented and a more precise characterization of a few selected motivational attitudes is given. The specification framework DESIRE for multiagent systems is characterized. A general agent model is described. The framework of modeling motivational attitudes in DESIRE is discussed.

Agents are assumed to have the four properties required for the weak notion of agency described in Wooldridge and Jennings (1995). Thus, agents must maintain interaction with their environment, for example observing and performing actions in the world: reactivity; be able to take the initiative: proactiveness; be able to perform social actions like communication, social ability; operate without the direct intervention of other (possibly human) agents: autonomy. Four main categories of mental attitudes are studied in the AI literature: informational, motivational, social and emotional attitudes. Examining the distinction between human

Cognition and machine-generated or also lower-level (human) cognition at Nourani et al. (2013), considering cognition, as being the “higher level capacity of discursive thought” in the sense of Kant “I think so I am,” these processes include thinking, knowing, remembering, deciding, and problem-solving. Cognition or cognitive processes, therefore, in general, can be natural or artificial, conscious or unconscious. Human Cognition is affected by mental, attitudes, as well as social and environmental conditions involving the person. Cognition design is essential to solving design problems in cognitive agent systems.

It is obvious that human Cognition is significantly different from any virtual or cyber-physical agent system that has to act in a prescribed environment as they are having inherent “machine” cognition capabilities. Cognitive intelligence uses a human mental introspective experience for the modeling of intelligent system thinking. Cognitive intelligence may use brain models to extract brain’s intelligence properties (or functions). On the other side modeling or designing the computational cognitive intelligence is still a very hard task to be solved, even if the progress in this area has been amazing. Cognitive intelligence in cyber-physical worlds takes a totally new dimension in terms of complexity and dynamicity of the emergent and interaction complex multiagent systems that include humans as well as objects, devices and complex applications or environments.