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 Intelligent systems, intelligent agents

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

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

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

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

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

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

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

“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

Properties - summary • General, not exact definitions - “ideal agent” – theoretical, nonexistentreal 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

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

Smart Agents Collaborative Learning Agents Cooperate Learn Autonomous Collaborative Agents Interface Agents Typology of agents

Typology of agents

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

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

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

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?

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)

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

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

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)

AI DEVELOPMENT, TECHNOLOGY Truly intelligent? Intelligent systems!

First Slovenian agents • 1993 IOI, interface VAX/VMS; B. Hribovšek, M. Gams • 1996 EMA, an employment agent for Slovenia on Internet, M. Gams, A. Karalič National Employment Office • 1998 Personal WebWatcher, D. Mladenič • 2000 ShiNa, A. Pivk • 2000 ActiveTools, USA, A. Bezek • 2007 MASDA, A. Bezek

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

Intelligent systems, intelligent agents