1. CIS 488/588 Bruce R. Maxim UM-Dearborn
Reactive Approach
CIS 488/588
Bruce R. Maxim
UM-Dearborn
12/8/2018

2. Reactive
For reactive AI the output is always the same for a given input (deterministic)
Its behavior is completely predictable for a specific situation
Nondeterministic reactive AI would require the addition of memory so that it current action could depend on its state and the current input (and even its last action in this state if you wish)
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3. Planning Basics
Planning is particularly desirable when a problem domain has a well-defined set of operators and easily determined states
Often times accomplishing a task is reduced to asking “which operators can be used?”
It is wise to pick operators that move you closest to your goal
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4. Basics
If there are obstacles the task is modified to become “how do we remove obstacles?”
Result of planning will be a plan in which the task accomplishing every goal has been reduced to a list of primitive operators
Often times planning and executive are interleaved
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5. Problem Reduction
Technique for achieving goals in a problem space by generating and achieving subgoals
When presented with a problem the problem reducer will need to find a plan to solve the problem
Finding a plan might be thought of as being a type of “heuristic search” in which the operators add detail by attaching plans for goals and coordinating steps
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6. Problem Reduction Coordination involves Good for top-level
Choosing harmonious plans for simultaneous subgoals
Choosing the order of execution for subplans
Good for top-level
Limitation of problem reduction is that it does not always take subplan interactions into account
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7. Algorithm Initialize graph to starting node
Loop until starting node is solved or until cost exceeds futility
Traverse graph using current best path and keep track of nodes on path not solved or not yet expanded
Pick an unexpanded node
if no successor then assign futility as node value
else add node successors to graph and estimate value of node
f(x) = y(x) + h’(x) // h’ is underestimator
note: nodes where h'(x) = 0 are solved
Update f'(x) of newly expanded node based on successor values
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8. Problem Reduction Advantage Disadvantage
Trades the old problem of finding an operator sequence (e.g. a path through states) for the new problem of coordinating operator sequences
Disadvantage
Requires more domain knowledge than heuristic search
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9. Problem Reduction
May require a large number of plans and the plans themselves may be large
Particularly useful if problem space can be represented as an and/or graph
Recall that and/or graphs are useful in answering how and why questions
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10. And/Or Tree Goal: Acquire TV Steal TV Buy TV and Earn Money Get Job
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11. Problem Reduction
Important to make plan finding phase as efficient as possible
How are plans/operators found?
Preconditions
Post conditions
Good candidate for top level program since it combines general plan coordination techniques with library of specific plans
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12. Planning in Games Plans can be built in a reactive manner
Pathfinding in uneven terrain is best done with planning techniques that search for the best available path
Pathfinding requires a world map and lots of storage
Reactive pathfinding would take longer, might not find the optimal path, but would use far less storage (only local terrain information is needed)
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13. Reactive Substitutes for Planning
There are very few cases in games where planning is necessary
Reactive strategies may appear very complex when used for high level tactical decisions
Obstacle avoidance could be viewed handled reactively as long as the agent avoids getting stuck
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14. Reactive Deterministic Game Techniques
Scripts (small programs used to compute a result given some input parameters
Rule-based systems (collections of if..then rules used to manipulate variables)
Finite-state machines (select next state action based on current input values)
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15. Reactive Advantages for Animats
Embodiment (reactive behaviors based on interpreting local information to select actions)
Learning (many learning techniques are based on acquiring reactive mappings between inputs and behavior)
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16. Architectures
Components are best viewed as black boxes with details of AI unknown to its users
Architectural Styles
Monolithic (single component architecture)
Flat (many components exist in parallel)
Hierarchical (components nested inside one another)
Reducing complexity is the deciding factor used to select an architectural style
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17. Game AI Decomposition Structural Behavioral Goal
Splits solution according to the function (movement, weapon handling) of each component
Behavioral
Based on system activities or modes (hunting, fleeing, collecting ammo) different component for each behavior (very appropriate for computer games)
Goal
Create goal trees to assist in decision making
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18. Component Connection Types Attribution
Independent sum (components connected to different outputs to avoid conflicts)
Combination (outputs from different components blended together to make final result)
Suppression (strong components get priority weak components ignored)
Sequential attribution (arbitration used to ensure output of different components over time – fair scheduling)
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19. Example - 1 Subsumation Horizontal, hierarchical layers
Higher layer outputs over ride lower layer outputs
Behavior-based decomposition
Uses flat organization
Output suppression
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20. Example - 2 Voting system
Set of distributed components connected to a smaller vote counting component
Output with the most votes wins
Uses functional decomposition
Flat organization
Combination used to merge outputs
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