A Multiagent Framework to Animate Socially Intelligent Agents

Lessons from research on interaction with virtual environments

“…Recent works (such as Chang et al, 2005;Grimaldo et al, 2006Grimaldo et al, , 2008aIbá ñez, 2004, described in Section 2.1) have proposed pretty similar layer architectures. Normally a middle (semantic) layer is the interface between the agents and the virtual world.…”

“…Requirements Cavazza (1998) Recognises a need for a knowledge representation layer for virtual environments Pioneering works Thalmann et al (1999) Informed environments based on a database model Urban life simulation Method for building virtual scenes with semantic information NOVAE Allongue, 1997, 1998) Semantic approach to increase VW interoperability Naval simulation (case study) (Soto and Allongue, 2002) Proposes the ontology paradigm Influence/reaction model for describing behaviours and reactions Doyle (2002) Introduces the concept of annotated environment (with structured representations of its contents and purpose) Agent architecture for interacting with the annotated environment Virtual objects with semantics Smart objects (Kallmann and Thalmann, 1999) Pre-programmed possible interactions Extended with action semantics expressed by rules, to reason about the consequences of actions (Abaci et al, 2005) Starfish (Badawi and Donikian, 2004) Synoptic objects Set of actions assigned to interactive surfaces Agents get the data from these interactive surfaces NiMMiT (Vanacken et al, 2007) Semantics incorporated in a diagram based notation intended to describe multimodal interaction Driving simulator (case study) Complete architectures with ontologies Chang et al (2005) Framework with an ontology-based cognitive middle layer between agent minds and the environment manages semantic concepts This layer also represents actions through causal rules Grimaldo et al (2006) Multi-agent framework composed of: ontologies, a semantic layer, planning based agents, and a 3D Engine Grimaldo et al (2008a) Ontology is used to define social relations among agents within an artificial society Virtual university bar (Grimaldo et al, 2008b) (Pellens et al, 2005) Necessity to let the domain expert participate in the specification of the VR application (Troyer et al, 2003) Virtual bowling game (Bille et al, 2004) and virtual shops (Troyer et al, 2007) Approach to design and develop a VR application where the domain expertise is used to generate it (Bille et al, 2004) Development process composed of three sequential steps: the specification step, the mapping step and the generation step (Pellens et al, 2005) method for building virtual scenes with associated semantic information as well as for the exploitation of such scenes. The three-dimensional scene provided by the designer is divided into two parts, one for visualization and another for database construction.…”

“…The proposed architecture is a multiagent framework, which can be divided into several parts: ontologies that define the world knowledge base; a semantic layer which is the interface between the agent and the world; planning based agents that receive sensorial information from the semantic layer and calculate the appropriate sequence of actions in order to achieve their goals; and a 3D Engine that extracts graphical information from the object database and performs visualization. In Grimaldo et al (2008a), the ontology is used to define social relations among agents within an artificial society. These relations must be taken into account in order to display socially acceptable decisions.…”

Recognizing and learning models of social exchange strategies for the regulation of social interactions in open agent societies

“…The works by Grimaldo et al [23][24][25][26][27] presented an interesting application of the Theory of Social Exchanges in the coordination of intelligent virtual agents and sociability in a virtual university bar scenario (in a 3D dynamic environment), modeled as a market-based social model, where groups of different types of waiters (e.g., coordinated, social, egalitarian) and customers (e.g., social, lazy) interact with both the objects in the scene and the other virtual agents. In [26], they presented a multi-modal agent decision making model, called MADeM, in order to provide virtual agents with socially acceptable decisions, coordinated social behaviors (e.g., task passing or planned meetings), based on the evaluation of the social exchanges.…”