Topic 3Reactive animat, search and obstacle avoidance

• Game’s “Physical” Environment• Machine Vision• Representing Space in the Game World• Movement in the Game World• Navigation in the Game World• Obstacle Avoidance• A Reactive Control System• Use of Guntactyx

Reading: Champandard Chapters 5,6,8 FEAR documentation GunTactyx readme and manual

Website links on graph search

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Game’s “Physical” Environment• We may distinguish between two aspects of a game environment:

Structure - topography of the environment as it constrains movement (physics and layout of walls, paths, obstacles etc.

Detail – graphical appearance of the game world and placement of objects which don’t impede movement

• What about players, NPCs and monsters? Really need to consider moving things as a third category, especially when interactions go beyond simply destroying everything you see

• Humans perceive the world mostly visually through detail, while AI sees the world a as simplified data structures and must interpret these as well as it can

• We can try to make an AI interpret the graphical world directly, as if it was seeing through eyes, but such machine vision has proven to be very difficult to program and expensive to compute (at least at a human level of skill)

• It is an important concept of nouvelle game AI that an animat should only have local, not global, knowledge of the game (like a human)

• Having complete, perfect knowledge of the world is not good for AI or games

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Machine Vision

• To get more sophisticated output information requires more complex processing. Eg. scene analysis to aid robot navigation – hard

• The output for that would be information enabling the robot to identify particular objects, or find their range and bearing, to help navigate around them

• Getting a machine to see is a traditional sub-discipline of AI• A typical system might involve a camera returning a digitised image,

interpretive software and some kind of output arrangement• Eg handwritten letter recogniser – easy

ASCII for ‘2’00110010

digitalcamera

patternrecognitionsoftware

data representationraw image

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Face recognition from security video is now a mainstream application of machine vision

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Representing Space in the Game World

• How space is represented is important

• 2D vs 3D – how the location of objects is encoded in the structure, not how the detail makes the world appear

• Discrete vs continuous – meaning whether objects are placed in a grid or matrix with a finite number of locations or not (eg chess vs marbles)

• Representation of time - also discrete (turn-taking) or continuous (stream of consciousness)

• Conversions – discrete vs continuous is a relative matter, since a fine enough unit size (grid or time-steps) may be considered continuous in practice

• In fact, all representations in computers must ultimately be discrete, approximating continuous to a greater or lesser degree!

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Movement in the Game World• At present, game engines provide a locomotion layer which abstracts basic movement actions away from the strategic control of direction

• Physics simulation is required to handle gravity, throwing, fire, water etc.

• Low level motion might nowbe handled by the AI

Simulation loop(walking, running)physics handles displacementanimation handles limb cycling

Integrationsignals from environmentalter behaviour as appropriate (eg falls)

Control(forward/backward, turns)signals from user orparameters via API fromthe decision-making AI

locomotion layer

Collision detectionPhysics in the game signals a collision halting(forward) motion

Image: Chris Bayliss

Representing Space in the Game World

• In a classical AI navigation experiment, travel paths in the world model might be represented at design-time as a graph, with nodes representing rooms and arcs representing passages or doors between them

• Finding an optimal path from a current location to a target was then a matter of search on the graph

• There are well-studied and good search algorithms available

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Search - Basics• Uninformed search algorithms simply follow a pattern to examine all nodes until one containing the goal is found (aka “brute force”)• Depth-first search - start at a root and explore as far as possible along each branch before backtracking until goal is found• Breadth-first search - start at a root and explore all neighbouring nodes, then for each neighbour, explore their unexplored neighbours, and

so on until goal is found

On this graph, starting at A, choosing left nodes before right and remembering previously visited nodes:

- Depth First Search visits nodes in this order: A,B,D,F,E,C,G

-Breadth First Search visits nodes in this order: A,B,C,E,D,F,G

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Search – Using Domain Information• Informed search algorithms use some method to choose intelligently which node to search next• Best-first search – modifies breadth-first method to order all current paths by a heuristic which estimates how close the end of the path is to a goal. Paths that are closest to a goal are extended first. • A* search - is a best-first method that calculates a cost and an estimate for the path leading to each node: • Cost is zero for the first node; for all others, cost is the cumulative sum associated with all its ancestor nodes plus the cost of the operation which reached the node (e.g. linear distance)• An estimate measures how far a given node is thought to be from a goal state (e.g. intensity on a sensory gradient)• Cost and estimate are summed to score each path for eligibility for exploration• For more detail, view the ‘Graph Search Methods’ link on the website

Navigation in the Game World

• There are problems with this kind of classical search however:– Depends on global information at design-time, so

– Question of realism arises – not comparable with the limited viewpoint of real biological creatures

– A lot of information may overwhelm decision-making processes

– Information does not update dynamically via sensors, so cannot track changes (eg moving creatures in the world)

• Nouvelle game AI animats are (virtually) embodied, which implies that• - They have a (simulated) limited perceptual system which updates the AI

continuously

- They need more plausible navigation algorithms which can work on limited information and in real time

• For now, we are interested in reactive solutions

• Fortunately, such solutions have been studied for the design of robots

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Modelling an Animat’s State in Space• For many (but not all) AI models, need a description of the animat’s

position and orientation in space, as well as how it will move

(0,0)World Origin

Animat(3,3)

Object(6,1)

Absolute Coordinate System (0,0)Egocentric Origin

Animat

Object(3,2)

Relative Coordinate System

Continuous moves and turns

any distance

any angle

Discrete moves and turns

unit distance

90 deg angles

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Animat brains in FEAR• In FEAR architectures, modules and interfaces are defined declaratively

and at a very high level in XML• This is supposed to make building and debugging easy• Yet for a standard animat in the Quake 2 demonstrator, the most

interesting specification is that of the animat’s brain, which is a C++ function defined in the file Brain.cpp called Think and that is procedural

• The following very basic navigating brain depends on calls to the physics, vision and motion modules

void Think(){ bool col = physics->Collision(); //check for touching contact

float front_obstacle = vision->Tracewalk (0.0f,8.0f); //any problems ahead?

//decide what actions to call motion->Move(Forward); if (col || front_obstacle < 2.0f) motion->Turn(orientation); // where orientation is some angle}

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Sensing and Acting in the World - FEAR• Physics module // returns true if animat has bumped something, false otherwise physics->Collision(); //Polledphysics->OnCollision(); //Asychronous callback detecting a collision

• Vision Module// returns the distance to nearest object at bearing anglevision->TraceWalk(angle, steps); //simulating walking on that bearingvision->TraceLine(angle); //as if throwing projectile on that bearing

• Motion module// continuous - angle is in deg. from current orientationmotion->Turn(angle);

// continuous - move in direction, a literal eg. Motion::Forwardmotion->Move(direction);

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Obstacle Avoidance – Basic functionality• Finding one’s way around obstacles is fundamental to navigation• Well suited to implementation by a reactive control system• Begins from general principles:

1. When no obstacle is sensed, move ahead normally (straight or wandering randomly)

2. If wall detected on one side, veer away slowly

3. If obstacle detected ahead, turn to one side to avoid it

4. When stuck in a corner, a more radical turn is needed 1

23

4

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A Reactive Control System

• A reactive system requires three elements:

• 1) a set of perceptual and action functions, which apply in a particular situation

• 2) a mapping showing which percepts release which behaviours. That means a theory about how the animat behave (note related to idea of behavioural laws for agents). See previous slide.

• An if-else-if-else structure could be used to order calls to perceptual and motor functions – procedural

• A rule-based system (Topic 6) is another possibility – declarative

• In FEAR we have synchronous calls, based on the client-server model. Requests by a client are made and do not return until the server has computed a result. Usually based on simple function calls. Commonly used for the delegation of tasks

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A Reactive Control System• Could also use asynchronous events. Based on something called the observer design pattern: have a set

of behaviours ready to go and a set of events that will trigger them. A kind of event-based processing

• These can interrupt another routine and transfer control when something comes up unexpectedly

• These should operate in ‘parallel’, competing for control of the animat’s body => need for 3) arbitration in case of a tie

• How are competing motor outputs combined to control the limbs?

- Independent sum – different outputs connected to different effectors so that they cannot clash

- Combination – some formula (eg weighted sum) combines two or more outputs to blend them into output commands

- Suppression – certain components get priority over others, and can replace their output with their own (eg. subsumption architecture)

- Sequential – output of different behaviours getting control at alternate times

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Subsumption architecture works on real robots!

Cruise

Follow

Avoid

Escape

Random Act

Photosensors

IR proximity

Bump switches

Motor command

Motor load s

s

s

s

sSensory triggers

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Heroes of AI #3 – The Radical • The radical idea that sophisticated robot behaviour could be accomplished without high-powered computing was advanced in the 1980s by ex-patriot Australian engineer Rodney Brooks. At the time robots were slow and clumsy, using classical planning to compute their motions

• Brooks argued that robots could interact directly with the world via properly designed reactive network of sensors and actuators, and created a number of behaviour-based control architectures for robots. Without the need for complex representations. In the 1990s he and his students at the MIT robotics lab demonstrated ever more ingenious robots that used his subsumption architecture. • Brooks was featured in a 1997 documentary called “Fast, Cheap and Out of Control’, the name of his paper to the British Interplanetary Society arguing that insect-like robots could be used for exploration of the solar system.

• Formed a company called iRobot, which now provides pack-bot robots to the US military as well as mass producing Rhoomba floor-cleaning robots

• Latest and most demanding project is Cog, a behaviour-based humanoid robot

How to Use Guntactyx• Guntactyx is a programming game - write scripts to control the behaviour of a

team of ‘bots

• Up to 4 teams can play different kinds of games, including fighting, racing and soccer – better to use two small teams on most machines

• Control is via the main front panel (but the options panel (‘0’) is better). Press F3 to get a "heads-up"-style overlay

• Scripts are written in a C-like language called Small. Use any kind of text editor to create and modify Small scripts (but choose one which does not insert special characters into the text eg. Notepad, not Word)

• Script files are saved into the \bots directory with the extension .sma

• Script files must be compiled using the Small compiler sc.exe before they will appear as a team on the main panel

• Read the GUNTACTYX_manual.htm for more detail

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Summary• Game virtual worlds generally distinguish structure and detail. Moving objects

could be considered a third category

• Humans see detail, but game characters generally interact via structure

• Machine vision is an important but difficult sub-field of AI

• The representation of space and time may be 2D or 3D, discrete or continuous

• Present game engines provide a locomotion layer which abstracts basic movement actions away from the strategic control of direction. In future AI may automate basic interactions with world

• Travel paths in through space may be represented as graphs. These are traditionally searched by search methods such as breadth-first, depth-first, or A* search. Such search is a general problem-solving method

• Reactive control systems require 1) perceptual & action functions 2) a mapping from percepts to actions representing a theory of behaviour and 3) a method to arbitrate conflicts – to resolve which action will be taken in case of a tie

• Could be implemented procedurally as if-else-if statement or declaratively as a set of rules in a RBS

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References

• Brooks, R. & Flynn, A. Fast, Cheap and Out of Control: The Robotic Invasion of the Solar System. Journal of The British Interplanetary Society, Vol. 42, 1989, 478-485.