Intelligence Support Approach

The cognitive component of the pedagogical agent is found to be important to provide intelligent functions that add value to learning support and new possibilities. The intelligent component of the pedagogical agent is important to:

 Provide a decision ability

 Control the character behavior

 Reason about the environment

 Provide alternatives for the learner

 Enable the IPA in general to act intelligently as possible

Several methods are found to support intelligence addition to the pedagogical agent such as case based reasoning, cognitive architectures, probabilistic reasoning models, neural networks, and more. While those several methods can support reasoning in pedagogical agents, the intelligent agent approach is elaborated for the following reasons:

 The intelligent agent approach provides individual properties to the pedagogical agent of pro-activeness, re-activeness, and reflexive behavior.

 The intelligent agent approach does not isolate an agent but provide methods to support multiple entities. In relation to traditional learning, individualized learning possibilities should not be the only case to assume while ignoring collaboration abilities. An opportunity arises if multiple agents can be depicted. While the research focused on an IPA as an individual entity focusing on the visual behavior of the pedagogical agent of animations and so on. The pedagogical aspects of collaboration worth consideration. With the intelligent agent paradigm, the collaborative learning aspect is extended. The promising utilization of multi-agent system, discussed in Section 5.5., for collaborative learning suggests that IPA can be within a multi-agent system as well.

 The multi-agent system is for a distributed environment. In dynamic, non-deterministic environments, autonomy of agents and dynamic reasoning on the situation suits that nature. The immersive environment is a dynamic non-deterministic environment. Multi-agent systems have been successfully deployed for similar vast distributed environments.

 The multi-agent approach can encapsulate other functions. As it is a distributed model, the component of each agent can represent a function. A constellation of those functions is achieved while maintaining each component desirables of autonomous behavior and non-determinism.

 Models of the intelligent agent to resemble the human think process are available. The Belief-Desire-Intention model of agents is rooted to a human thought process in how intentions and desires a human can take in relation to the beliefs (Bratman, 1987). The model is targeting an action approach that also is desirable for the IPA to act in the environment.

 If the IPA provides action-orientation, this contributes to the presence by the IPA and adds to its believability. An environment is inhabited not only by embodiments, but also by increased interaction and provided support. The actions of the IPA are not only reflexive in relation to learner requests but also should be proactive by nature.

In the research literature surveyed in Chapter 5, intelligent agent systems have been used for human learning purposes either individually as pedagogical agents, collectively in agent societies, or within virtual learning environments. The use of those agents can provide different learning functions (Soliman & Guetl, 2011c).

Those agents can be depicted as:

 Agents for learning personalization. Those agents promote learning through understanding individual learning abilities and treating the learner accordingly.

 Agents for emotional support: Those agents support learning through improving engagement and motivation in the learning environment through considering the learner emotional state and improving it accordingly.

 Cognitive agents. Those agents are inspired from cognitive theories of the human mind as well as AI.

 Meta-cognitive agents. Those agents are concerned with higher levels of thinking by including meta-cognition supporting methods such as communicating by concept maps.

 Teachable agents. Those agents improve learning through giving the human learner the ability to teach an artificial pedagogical agent.

 Self regulated learning agents. Apply self-regulated learning theories by agents.

 Conceptual change agents. Agents that consider conceptual change learning theories.

 Explainable agents. It is discussed in Section 5.3.2 that tracking the reasoning processes of the pedagogical interactions can provide an input to learning.

Explaining behavior is possible with the BDI model (Broekens et al., 2010).

 Multiple agents supporting group learning or training. It has been shown the benefits of intelligent agents in collaborative learning setting. Thus adopting an intelligent agent approach enables achieving those functions in virtual worlds.

Considering available agent frameworks, it is needed to look into further functional perspectives. The agent based implementation of those functions is taken individually by different research groups. Definitely, adopting most of

those functions simultaneously in the virtual environment is desirable with need of considering agent frameworks.

An Agent-based design is a major design decision that has a relation to the environment as well. As the environment is inhabited with various learning resources and immersed learners, several pedagogical agents are required. And hence it should be better assumed that pedagogical agents are not working in isolations. The Multi-Agent System (MAS) model provides an approach for communication between agents at several levels in the virtual world. Research works targeted employing the MAS model in 3D virtual worlds. The work by Panayiotopoulos, Katsirelos, Vosinakis, and Kousidou (1998) provided a proof-of-concept of an agent architecture that works in VRML-based virtual worlds.

Benefits of this model include reasoning abilities and the ability of extension to improve collaborative work. In this work the agent is an autonomous avatar that can find his way in a 3D maze. The project of River City in Virtual Singapura (Yu, Shen, & Miao, 2007) recognizes the importance of employing an agent-based model that uses goal-net architecture. A goal-net is a hierarchy of goals that agents need to go through to achieve a bigger goal. In this project the avatar is equipped with goal-net agents (see Figure 16)¹.

Figure 16: Agent controlled 3D avatars in the River City project (Yu, Shen, & Miao 2007).

Furthermore, the work by Canales‐Cruz, Sánchez‐Arias, Cervantes‐Pérez, and Peredo‐Valderrama (2009) denoted the importance of employing an MAS model in the VLE for learning architectures to achieve several gains relevant to the sociability nature of the environment. It also denoted its importance for the cooperation in the environment. It coincides with work towards collaborative learning functions in the distributed learning environment by means of intelligent agents. This study suggests a conceptual view of the IPA working in an

1 Paragraph has partial adoption from Soliman and Guetl (2010c).

immersive VLE to reflect new possible scenarios for learning in the environment and to provide intelligent pedagogical functions in the immersive environment¹.