1. Intelligent systems, intelligent agents New AI directions: cognitive and applications Advantages: adaptable, flexible, able to learn, user- friendly, “bluff” intelligence A typical agent: insurance agent (M. Minsky); many users Other types of agents: art. life, static-mobile, distributed, for people or computers Intelligent information society

2. INTELLIGENT AGENTS Examples Internet - filtering, browsing, e-commerce,.... : PC - system agents, Office Hundreds of agents, more important – agent approach = more advanced, more powerful, more modern

3. Definition of intelligent agent No reasonable definition of intelligence - no theoretical definition of intelligence succeeded (empirically failed) - intelligence might be a stronger (non- computational) concept No reasonable definition of intelligent agents Humans capable of easily distinguishing between (non)intelligent subjects, and between agents and non-agents

4. What are agents? Diverse and complex types of agents (most important are common principles) Diverse and complex application domains Internal structure is not essential (although usual AI-based) Outside performance is important (like expert systems) No reasonable definition of intelligent agents Humans capable of distinguishing between agents and non-agents, and the power and amount of agentness

5. Properties of intelligent agents autonomy - ability to perform tasks and decisions without direct intervention of humans social ability, ability to interact with humans and agents responsiveness, the ability to perceive the environment and respond to changes

6. proactiveness, the ability to take initiative and to exhibit goal-directed behavior adaptability, the ability of an agent to modify its behavior mobility, the ability to change physical location veracity, assumption of no false information rationality, ability to perform reasonably

7. Properties - Pattie Maes Observes a user Gets feed-back from a user Gets direct instructions from a user Gets experience from environment Agent and user communicate, control, execute Agent learns according to interests, wishes and desires of users

8. Properties - Etzioni Autonomy when executing tasks; gets task descriptions from a user, modifies it, performs it on its own Time continuous – work all the time Personality - speak too much Able to communicate To adapt to each single user – personalization Mobility

9. “Simple rules” does it perform typical user-oriented functions (insurance agent) autonomy - performs actions on its own (yes) - is prediction of actions possible (no) adapts to each specific user - different reactions for the same error works all the time, looks around (mobile) data - information - knowledge

10. Properties - summary General, not exact definitions - “ideal agent” – theoretical, nonexistent real agents only approximations with some properties - borders soft, not exact Agent is a (slightly) different program Similar relations: non/structured programming; modular/object; information systems/operation systems/expert systems; data/information/knowledge some people don’t understand the difference

11. Types of agents - Etzioni Co-drivers – suggest where to go to Drivers – listen to suggestions by users Secretary-assistant, gets strategic directions and performs actions on its own

12. LearnCooperate Autonomous Collaborative Learning AgentsSmart Agents Interface AgentsCollaborative Agents Typology of agents

14. Types of agents simple reflex agents condition-action, pattern-based model-based reflex agents + model of the world (partial) goal-based agents + goals (desired states, boolean) utility-based agents + utility learning agents + learn

15. Types of agents Decision Agents Input Agents Processing Agents Spatial Agents (physical real-world) Believable agents (artif. character) Physical Agents (e.g. robot) Temporal Agents

16. Types of agent environments → More complex Observable - Partially observable Deterministic - Stochastic Episodic - Sequential Static - Dynamic Discrete - Continuous Single-agent Multiple-agent

17. 17 MAS Multi-agent Systems (MAS) A MAS is one that consists of a number of agents, which interact with one-another In the most general case, agents will be acting on behalf of users with different goals and motivations To successfully interact, they will require the ability to interact with each other, much as people do Can you think of an example?

18. 18 MAS Multi-agent Systems (MAS) Autonomous software agents Local view Decentralization Self-organized Often use Knowledge Query Manipulation Language (KQML) or FIPA's Agent Communication Language (ACL) Often use Knowledge Query Manipulation LanguageFIPA's Agent Communication Language

19. 19 MAS STUDIES agent-oriented software engineering beliefs, desires, and intentions (BDI)BDI cooperation and coordination organisation communication negotiation distributed problem solving multi-agent learning scientific communities dependability and fault-tolerance

20. 20 MAS FRAMEWORKS Jade (Java) Jade Repast (Java) Repast Swarm (Objective-C) Swarm NetLogo (Logo) NetLogo MASON (Java) MASON SemanticAgent (SWRL) on top of JADE SemanticAgent Wikipedia

21. Bill Gates.. computer of the future - an intelligent computer assistant, a kind of secretary, capable of communicating and executing simple mundane tasks. The new system will be capable of talking, listening, seeing, and will have other anthropological features like faces capable of expressing gestures. (agents are the right direction)

22. Truly intelligent? Intelligent systems! DEVELOPMENT, TECHNOLOGY AI

23. First Slovenian agents 1993 IOI, interface VAX/VMS; B. Hribov š ek, M. Gams 1996 EMA, an employment agent for Slovenia on Internet, M. Gams, A. Karali č National Employment Office 1998 Personal WebWatcher, D. Mladeni č 2000 ShiNa, A. Pivk 2000 ActiveTools, USA, A. Bezek 2007 MASDA, A. Bezek

24. CONCLUSION Intelligent agents are among the most prospective new SW breeds; Intelligent agents represents a marriage between AI, intelligent systems, and information society