Artificial Intelligence Simulation Engines 2008 Chalmers University of Technology Markus Larsson markus.larsson@slxgames.com 08-11-24 Simulation Engines 2008, Markus Larsson 1
History of AI AI concerns itself with understanding intelligent entities Unlike psychology or philosophy, AI deals with how to build these intelligent entities as well Young research area Defined in 1956 However, has connections to definitions by classic Greek philosophers such as Plato and Aristotle Has gone through many turbulent phases Almost childish enthusiasm in the early days Depressive state after a while More realistic outlook today 08-11-24 Simulation Engines 2008, Markus Larsson 2
AI in games Most computer games are played against some form of opponent When not playing against another human, in many cases a computer-controlled opponent is needed AI in games provide the human player with a challenging opponent or ally without requiring the presence of another human A keyword is “challenging” Not necessarily proficient or complex Do not use unnecessarily advanced techniques Often the AI needs to be tunable for different difficulty levels There are no style points for being true to the field of AI. Cheap tricks are good 08-11-24 Simulation Engines 2008, Markus Larsson 3
Agents An agent is an autonomous and independent entity that, much like a human being: Collects information about its surroundings Draws conclusions Makes decisions Executes actions Very useful in AI Easy and natural semantical conception of a sentient game entity Lends themselves well to object-oriented design 08-11-24 Simulation Engines 2008, Markus Larsson 4
A model for AI in games A useful model for our continued discussion Perception The agent collects information about the surrounding using its “senses” Decision The agent analyzed the collected data, builds an understanding of the situation and then makes a decision Action Given the decision, translates into a number of separate steps needed to accomplish the goal 08-11-24 Simulation Engines 2008, Markus Larsson 5
Cheating The golden rule of games AI Cheat as much as you can get away with! There is no incentive for AI programmers to “play straight” The job is to create a worthy opponent No rules of conduct Cheating can be done in all parts of the model 08-11-24 Simulation Engines 2008, Markus Larsson 6
Cheating Perception The most basic cheat is to give the computer access to an internal representation of the world instead of having it interpret the world itself It is often useful to give the AI more information than what the player has access to (exact positions etc) Decision More difficult are with cheating Agents that are not visible to the player can often entirely ignore the decision phase 08-11-24 Simulation Engines 2008, Markus Larsson 7
Action Action It can often be useful to work on different sets of rules than players For instance, a computer-controlled combat pilot might use a more simplified flight model than human players If you cheat, make sure it is not obvious to the player! The ultimate cheat is to script behaviors 08-11-24 Simulation Engines 2008, Markus Larsson 8
Perception Perception provides the agent with information about its surrounding environment using sensors Can be anything from a simple photosensitive sensor that detects light to a full vision system In the context of games, the agents rarely perceive the environment on their own, instead they look at the common scene graphs etc Topics of interest Identify different ways to access an enemy position Find places to hide Identify threats (windows, doors, etc) 08-11-24 Simulation Engines 2008, Markus Larsson 9
Perception Perceptive tasks depend on the type of game of course, but tasks tend to relate to the perception of the topology of the environment Manual topology markup Level editors add topological information to the 3D world (places to hide, places to shoot, patrol routes, pathfinding information, etc) Automatic topology analysis Analysis of the world to automatically identify access points, paths, hiding places, etc Often at least partially done in a pre-processing step 08-11-24 Simulation Engines 2008, Markus Larsson 10
Decision Given our input from the perception phase, we want to make a decision Finite state machines Rule-based transition system Fuzzy state machine Rule-based transition system based on fuzzy logic Artificial life Simulation of artificial life forms and behavior Neural network Network-structure for interpreting input and giving output (learning architecture) 08-11-24 Simulation Engines 2008, Markus Larsson 11
Action In the decision phase, we come up with a general decision for our high-level plan In the action phase, we execute the plan through low-level actions Example An AI infantry commander comes up with the decision to “take hill 241”. The action phase then translates this into first finding the shortest path to hill 241 (staying in cover from enemy fire), issuing the movement commands to his soldiers, assuming a combat formation when approaching the hill, and then taking a defensive position once the hill has been secured. Pathfinding Finding the shortest path from point A to point B given a number of constraints. Might need to take coordination between multiple agents in consideration Multi-level agents Modern AI systems often need multiple layers for controlling low-level and high-level actions 08-11-24 Simulation Engines 2008, Markus Larsson 12
Finite state machines Suitable technique for implementing simple rule-based agent Consists of a set of states and a collection of transitions for each state A transition consists of a trigger input, which initiates the state transition, a destination state, and an output The FSM also has a start state from which it begins execution FSM's are often drawn as state transition diagrams 08-11-24 Simulation Engines 2008, Markus Larsson 13
FSM's for AI Benefits Good control over the agent's behavior Easy to implement Model is easy to understand for designers Drawbacks Hard (and time-consuming) to write exhaustively No emergent behavior; the agent will only do what we tell it to do. We can not hope to get holistic effects of rules acting together Deterministic: agent is easy to predict and its behavior could potentially be exploited 08-11-24 Simulation Engines 2008, Markus Larsson 14
Example: Finite state machine 08-11-24 Simulation Engines 2008, Markus Larsson 15
Fuzzy logic One of the main features of a FSM is that it is deterministic A desirable effect in many systems Not necessarily a good thing in AI Can create predictable behavior Natural solution Make our FSM non-deterministic For a given input, any output can be chosen by random or by an internal weighting function Fuzzy state machine 08-11-24 Simulation Engines 2008, Markus Larsson 16
Autonomous agents Definition from Russel & Norvig, 1995 An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment using effectors Works very well with the model we have previously mentioned 08-11-24 Simulation Engines 2008, Markus Larsson 17
Autonomous agents Autonomous agents take the information they perceive into account when forming and carrying out its decision Non-autonomous agents simply discard sensory input We will examine three types of agents Reactive agents Reactive agents with state Goal-based agents 08-11-24 Simulation Engines 2008, Markus Larsson 18
Reactive agents A reactive agent is the simplest form of agent and reacts to a situation purely according to a set of rules for action and reaction For each update, the agent searches its database of rules until it finds one that matches the current situation, then executes the appropriate action associated with the rule Can be easily implemented using FSM 08-11-24 Simulation Engines 2008, Markus Larsson 19
Reactive agents with state In many cases, it is not sufficient to base behavior on input alone We might need some kind of state (memory) Example A driver looks in a rear-view mirror from time to time. When changing lanes, the driver needs to take both the information from looking in the mirror and the information from looking forwards in consideration. 08-11-24 Simulation Engines 2008, Markus Larsson 20
Goal-based agents Sometimes state and rules are not sufficient We need a goal to decide the most useful course of action Gives rise to goal-based agents which do not only have a rule database, but also select actions with a higher-level goal in mind Implies that the agent needs to know the consequences Y of performing an action X The decision process becomes one of searching or planning given a set of actions and consequences Goal-based agents allow for emergent behavior 08-11-24 Simulation Engines 2008, Markus Larsson 21
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