A constraint-based reasoning system is used for knowledge representation and reasoning in behavioural animation, specifically in the animation of sailing behaviour. The object-oriented ECHIDNA reasoning and constraint logic programming shell handles the constraints for formulation and execution of plans for intelligent entities. At higher levels of control, the observed motion of an object is a reflection of the reasoning process of an intelligent entity as it reacts to its environment. The environment, internal knowledge and physical structure serve as constraints in developing a plan, which in turn provides additional constraints in the animation of reactive behaviour. An animation approach using constraint-based reasoning is presented focusing on details of the planning process adapted in the approach. The implementation of the NSAIL program reveals further insight into applying this approach towards the development of a high-level intuitive interface for behavioural animation. >

Abstract Behaviour is a reflection of a reasoning process that must deal with constraints imposed by an external environment, internal knowledge and physical structure. This paper proposes a framework for behavioural animation that is based on the next generation of object‐oriented, constraint‐based expert systems technology, and applies a control structure of knowledge agents and knowledge units to determine the behaviour of objects to be animated. Knowledge agents are responsible for planning, plan implementation and information extraction from the environment. The activity of an agent is dependent on the knowledge units ascribed to them by the animator. The interaction between agents and knowledge units is resolved by the reasoning engine, and thus, influences the eventual motion displayed. An example given is NSAIL, a pilot implementation using the model‐based ECHIDNA constraint logic programming shell. With this approach, the motion for a sailing scenario and other behavioural domains can be specified at a high level through the characterization of the knowledge agents.

There have been many proposals for adding sound implementations of numeric processing to Prolog. This paper describes an approach to numeric constraint processing which has been implemented in Echidna, a new constraint logic programming (CLP) language. Echidna uses consistency algorithms which can actively process a wider variety of numeric constraints than most other CLP systems, including constraints containing some common nonlinear functions. A unique feature of Echidna is that it implements domains for real‐valued variables with hierarchical data structures and exploits this structure using a hierarchical arc consistency algorithm specialized for numeric constraints. This gives Echidna two advantages over other systems. First, the union of disjoint intervals can be represented directly. Other approaches require trying each disjoint interval in turn during backtrack search. Second, the hierarchical structure facilitates varying the precision of constraint processing. Consequently, it is possible to implement more effective constraint processing control algorithms which avoid unnecessary detailed domain analysis. These advantages distinguish Echidna from other CLP systems for numeric constraint processing.

Schema-based representations for visual knowledge are integrated with constraint satisfaction techniques. This integration is discussed in a progression of three sketch map interpretation programs: Mapsee-1, Mapsee-2, and Mapsee-3. The programs are evaluated by the criteria of descriptive and procedural adequacy. The evaluation indicates that a schema-based representation used in combination with a hierarchical arc-consistency algorithm constitutes a modular, efficient, and effective approach to the structured representation of visual knowledge. The schemata used in this representation are embedded in composition and specialization hierarchies. Specialization hierarchies are further expanded into discrimination graphs.>