Lecture

• challenges in AI (or computer science in general): scale, autonomy, delegation, cooperation
• MAS … field of AI interested in designing systems of autonomous interacting entities
• course overview
  • general introduction
  • agent interactions: communication, game theory
  • distributed problem solving
  • agent-based modelling and simulation (course, case study, tutorials)
  • human factors in MAS
  • collective decisions in MAS: theory of social choice
• agent … perceiving and acting
• autonomy (but autonomous agent still could decide randomly)
• rational agent
• 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
• physical vs. communicative actions
• agent architectures
  • simple reactive agent
  • agent with an internal state
  • goal-based agent
  • utility-based agent
    • in case of conflicting goals or uncertain effects
• performance vs. utility
• 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
• finding a balance between reactivity and proactivity (so that we don't get distracted)
• applications
  • distributed problem solving
  • agent-based modelling and simulation
• Gama, Netlogo
• reactive agent vs. cognitive agent
  • cognitive agent can reason about the environment
  • reactive agent can only move randomly and perform some reactive actions
• 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
  • beliefs
    • representation of the world
    • possible incorrect and incomplete
  • desires
    • our goal
  • intentions
    • how to achieve the goal
    • intentions lead to actions
    • in a dynamic environment, we need to reconsider the intentions
      • we need to find a balance, we cannot be to indecisive
      • intention reconsideration – 3 commitment strategies
  • BDI goal-plan diagram (tree)
    • list of all plans we can use to achieve the goal
    • each plan consists of several atomic actions
    • example: gold miners
      • goal: earn money
      • plans: sell gold | other job
      • how to sell gold: have gold & go to market & sell
      • how to have gold: steal gold | pick gold
    • on every level of the tree, we switch between AND and OR
  • BDI pros & cons
    • grounded in philosophy, formal logic
    • high level of abstraction, captures folk psychology
    • explains why an agent does something
    • extensible, flexible, robust, failure recovery
    • dedicated programming frameworks (example: GAMA)
    • but complex, may be computationally slow
• 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
  • reactive and deliberative layers
    • obstacle avoidance can be reactive
  • how to handle interaction between layers?