Design of Multi-Agent Systems Teacher Bart Verheij Student assistants Albert Hankel Elske van der Vaart Web site (Nestor contains a link)

Overview Agents, multi-agent systems This course Views of the field Objections Agents & objects Intentional systems Abstract architecture

Russell & Norvig An agent is anything that can be viewed as perceiving its environment through sensors and acting upon that environment through effectors.

Michael Wooldridge An agent is a computer system that is situated in some environment and that is capable of autonomous actions in order to meet its design objectives.

Some agents Mars Path Finder Air traffic control Personal digital assistant P2p file sharing Game agents

A natural kinds taxonomy of agent (Franklin and Graesser)

Multi-agent systems A multi-agent system is one that consists of a number of agents, which interact with one-another This requires the ability to cooperate, coordinate, and negotiate with each other

Multi-agent systems How can cooperation emerge in societies of self- interested agents? What kinds of languages can agents use to communicate? How can self-interested agents recognize conflict, and how can they (nevertheless) reach agreement? How can autonomous agents coordinate their activities so as to cooperatively achieve goals?

Reactivity A reactive system is one that maintains an ongoing interaction with its environment, and responds to changes that occur in it (in time for the response to be useful)

Proactiveness A proactive system is one that generates goals and attempts to achieve them by taking initiatives and recognizing opportunities

Balancing reactive and goal-oriented behavior Timely response to changing conditions Systematically working towards long-term goals

Influences and inspiration Economics Philosophy Game Theory Logic Ecology Social Sciences

Overview Agents, multi-agent systems This course Views of the field Objections Agents & objects Intentional systems Abstract architecture

This course: examination 50% Exam about the course book Wooldridge’s An Introduction to Multiagent Systems chs 1-4, % Programming exercises To be submitted to using certain naming conventions 25% A presentation

This course: schedule Calendar weekLecturePresentationsComputer lab Deadlines 36Introduction ch1, ch2ch3, ch4Exercise 1 37Multi-agent interactions ch6To be scheduledExercise 1 38Reaching agreements ch7To be scheduledExercise 2 39Communication ch8To be scheduledExercise 2 40Working together ch9To be scheduledExercise 3 41Methodologies ch10To be scheduledExercise 3 42Applications ch11To be scheduled(Extra) EXAM (not about ch5, ch12)

This course: time investment 5 ECTS = 140 hours 140 hours/10 weeks = 14 hours per week 6 contact hours (2 hours lecture, 2 hours presentations, 2 hours computer lab) 4 hours self study 2 hours presentation (10 hours of study, 4 uur slide design / 7 weeks) 2 hours programming (so 4 programming hours per week when the lab session is included)

Overview Agents, multi-agent systems This course Views of the field Objections Agents & objects Intentional systems Abstract architecture

Some views of the Field Multi-agent systems as a paradigm for software engineering Interaction is probably the most important single characteristic of complex software Multi-agent systems as a tool for understanding human societies Social simulation, “theories of the mind” Multi-agent systems as a search for appropriate theoretical foundations “Neat” vs “scruffy”; theory vs engineering

Overview Agents, multi-agent systems This course Views of the field Objections Agents & objects Intentional systems Abstract architecture

Objections to MAS Isn’t it all just distributed/concurrent systems? –Agents can be self-interested, so their interactions are “economic” encounters. There is no global goal. Isn’t it all just AI? –Agents may not need much intelligence –Classical AI ignored social aspects of agency.

Objections to MAS Isn’t it all just economics/game theory? –These fields ignored computational constraints and resource-bounded decision making Isn’t it all just social science? –Actual societies may not be optimal

Overview Agents, multi-agent systems This course Views of the field Objections Agents & objects Intentional systems Abstract architecture

Agents and objects Are agents just objects by another name? Object: –encapsulates some state –communicates via message passing –has methods, corresponding to operations that may be performed on this state

Agents and objects Main differences: –Agents are autonomous: they decide for themselves whether or not to perform an action on request from another agent –Agents are smart: they are capable of flexible (reactive, pro-active, social) behavior, and the standard object model has nothing to say about such types of behavior –Agents are active: a multi-agent system is inherently multi-threaded, in that each agent is assumed to have at least one thread of active control

Objects do it for free… Agents do it because they want to Agents do it for money

Overview Agents, multi-agent systems This course Views of the field Objections Agents & objects Intentional systems Abstract architecture

Agents as intentional systems Dennett: an intentional system is an entity ‘whose behavior can be predicted by the method of attributing belief, desires and rational acumen’ McCarthy: ‘Ascription of mental qualities is most straightforward for machines of known structure such as thermostats and computer operating systems, but is most useful when applied to entities whose structure is incompletely known’

Agents as intentional systems For more complex systems, we need more powerful abstractions and metaphors to explain their operation — low level explanations become impractical. The intentional stance is such an abstraction.

Overview Agents, multi-agent systems This course Views of the field Objections Agents & objects Intentional systems Abstract architecture

Abstract architecture for agents Environment states: Actions of agents: A run:

Agents, environments, systems An environment is a triple Env =  E,e 0,  where: E is a set of environment states, e 0  E is the initial state, and  is a state transformer function An agent is a function which maps runs to actions: A system is a pair  Env, Ag .

Some variations A deterministic environment: A reactive agent: Ag: E → Ac A state transition function that is independent of history: T : E  Ac  (E)

Agents with perception Environment Agent seeaction

Agents with internal states Environment Agent seeaction next state

Agents with tasks Utility functions can be used to tell an agent what to do without telling how to do it The task of the agent is to bring about states that maximize utility

Utility in the Tileworld

Optimal agents P(r | Ag, Env) denotes the probability of run r for agent Ag and environment Env An agent is optimal when it maximizes expected utility

Task specification using predicates Predicates Ψ: R → {0, 1} can be used for task specification: Ψ(r) = 1 expresses that an agent has succeeded, Ψ(r) = 0 that that it has not. An agent succeeds in a task environment (Env, Ψ) when

Types of tasks Achievement tasks Achieve a state of affairs Reach a state in a set of goal states G: Ψ(r) if and only if r contains a state in G Maintenance tasks Maintain a state of affairs Avoid a set of failure states B: Ψ(r) if and only if r does not contain a state in B

Overview Agents, multi-agent systems This course Views of the field Objections Agents & objects Intentional systems Abstract architecture