Lecture: kartičky | multi-agent-systems

course overview

another definition: agent

physical or virtual entity, has its own resources, possibly a representation of its environment
acts autonomously to satisfy some tendencies (goals) … proactive
while taking into account its limited resources, perceptions etc. … reactive

agent architectures

properties of environments

accessibility
- are all the relevant aspects of the environment instantly available to the agent? → we don't need to maintain an internal state
determinism
- is the next state completely determined by the current state and the actions selected by the agents?
accessible + deterministic → no uncertainty
episodic vs. sequential environment
- do future states depend on past actions? → sequential environment
- episodic environments are simpler, agents can make reactive decisions and do not need to remember the history (example: old chatbots)
static vs. dynamic environment
- dynamic environment can change during deliberation → we need to think fast (example: autonomous car)
- semi-dynamic environment does not change during deliberation but the passage of time is important (e.g. performance score)
- static environment does not change during deliberation, time does not matter
discrete vs. continuous
- discrete environment – limited number of distinct, clearly defined percepts and actions

examples

chess: accessible, deterministic, sequential, static or semi-dynamic (with a clock), discrete
poker: inaccessible, indeterministic, sequential
taxi driving: inaccessible, indeterministic, sequential, dynamic, continuous

applications

reactive agent vs. cognitive agent

practical reasoning

action-oriented reasoning = process of deciding what to do (not what to believe or what is true)
human practical reasoning
- deliberation – what state of affairs I want to achieve?
- means-end reasoning – how do I get there?
intentions in practical reasoning
- effort and resources put into achieving intention
- no conflict: intentions serve as a filter for adopting new intentions
- success tracking + persistance after failure
- possible – intentions are believed to be possible
- feasibility – not believed to never be reached
- …
Bratman's theory

BDI architecture

reflex architecture

stimulus-action rules
directly triggered by stimuli in the environment
less complex than symbolic AI
example: ants
- go back to nest = follow pheromones the other way
finite-state machines
subsumption architecture
- hierarchy of competence modules (CM)
- each module is responsible for a concrete, clearly defined, simple task
- all modules operate in parallel
- rather simple rule-like structure
- lower layers can inhibit higher layers
  - by suppressing input signals or inhibiting output signals
example: Mars explorer
- highest priority: obstacle avoidance
- drop carried samples at base
- return to base when carrying sample
- collect found samples
- lowest priority: exploration
how can reactive agent locate the base?
- gradient field – the base emits radio signal
how can robots communicate together?
- we want to tell the other robots where the clusters are
- we use “feromones” – stigmergy (indirect communication through environment)
  - robots drop radioactive crumbs
  - other robots need to pick them to make the trace “fade away”
- https://nausikaa.net/wp-content/uploads/2022/09/steels-mars-explorer-02.html
for reactive agents, the environment has to be accessible

hybrid architectures