Game and AI Week 4 Lab 2
Knowledge: As my games is based around exploration, I believe it will need some background knowledge on how humans would interact with the world, and how I would expect other humanoids that are not human to behave. Also, as I hope to have some animal mechanics, pack behaviour would also be an element of knowledge that would improve the game; and create immersion due to player mental models of game artefacts.
Knowledge representation: One way to represent knowledge is through Computer Graphics (CG), especially animations, based around FSM or HFSM systems; and then additionally through movement. knowledge can then be represented in code through behaviour models such as action selection - which itself is encapsulated by cognitive models, in which variables can be used to hold cognitive states, that then trickle down and set behaviours. However, knowledge can also be presented by sensory input from the world - using game objects such as colliders, and communication through messages from class to class. Knowledge, can also be represented through levels of memory persistence – I hope to use short working memory, only working with retaining memory that is relevant to each classes activity. In terms of semantics, I expect to use a grid system for path finding, which essentially represents the ability to navigate. The knowledge itself, will be stored as facts, leading to the ability to switch between states and change behaviour easily, based on boolean conditional statements. In contrast to this, knowledge can additionally be represented as emotions, for instance by hiding the state of variables and having the camera or the player avatar react to variable values, creates some confusion and surprise in the player – as the player tries to confirm, what the state of the in-game object is; which can be the player avatar or NPC elements of the game.
