ARTIFICIAL INTELLIGENCE [INTELLIGENT AGENTS PARADIGM] INTRODUCTION Professor Janis Grundspenkis Riga Technical University Faculty of Computer Science and Information Technology Institute of Applied Computer Systems Department of Systems Theory and Design E-mail: Janis.Grundspenkis@rtu.lv

Why Would You Study Artificial Intelligence? (1) Artificial intelligence is quickly emerging from the laboratory and is venturing into the commercial marketplace. Its impact on society is growing rapidly: in speech and language technology, strategic planning and diagnosis, process and system control, vision and authentication systems, information retrieval and data-mining and many other contexts. The many new realizations continually redefine which applications we can achieve and push existing technology to its limits

Why Would You Study Artificial Intelligence? (2) Reasoning with knowledge is a central issue. The mere fact that knowledge is power makes the importance of AI indisputable Due to the rapidly expanding role of AI in our current and future society, there is an urgent need for academically trained people with the variety of backgrounds who are familiar with the fundamentals of AI, aware of its reasonable expectations, and have practical experience in solving AI problems

Text Books Russell S., Norvig P. Artificial Intelligence. A Modern Approach, Pearson Education, 2010 Wooldridge M. An Introduction to MultiAgent Systems, John Wiley and Sons, 2009 Hadzic M., et al. Ontology-Based Multi-Agent Systems, Springer-Verlag, 2009

What Is Artificial Intelligence? (1) WHAT IS INTELLIGENCE? It is only a word that people use to name those unknown processes with which our brains solve problems we call hard (Minsky) Working definitions of what intelligence is must necessarily change through the years. We deal with a moving target which makes it difficult to explain just what it is we do

What Is Artificial Intelligence? (2) In principle, we should be able to build intelligent machines someday because our brains themselves are machines! One problem is that we know very little about how the brain actually works Even though we do not understand how the brain performs many mental skills, we can still work toward making machines that do the same or similar things Artificial Intelligence is simple the name we give to that kind of research

Different Approaches to AI (1) SYSTEMS THAT ACT LIKE HUMANS The act of creating machines that perform functions that require intelligence when performed by people (Kurzweil, 1990) The study of how to make computers do things at which, at the moment, people are better (Rich and Knight, 1991)

Different Approaches to AI (2) SYSTEMS THAT THINK LIKE HUMANS The existing new effort to make computer think … machines with minds, in the full and literal sense (Haugeland, 1985) The automation of activities that we associate with human thinking, activities such as decision-making, problem solving, learning … (Bellman, 1978)

Different Approaches to AI (3) SYSTEMS THAT THINK RATIONALLY The study of mental faculties through the use of computational models (Charniak and McDermont, 1985) The study of the computations that make it possible to perceive, reason and act (Winston, 1992)

Different Approaches to AI (4) SYSTEMS THAT ACT RATIONALLY Computational intelligence is the study of the design of intelligent agents (Poole et al., 1998) AI … is concerned with intelligent behavior in artifacts (Nilsson, 1998)

Acting Humanly (1) THE TURING TEST APPROACH The Turing test, proposed by Alan Turing (1950), was designed to provide a satisfactory operational definition of intelligence The computer would need to possess the following capabilities: Natural language processing Knowledge representation Automated reasoning Machine learning

Acting Humanly (2) THE TOTAL TURING TEST The computer additionally would need the following capabilities: Computer vision Robotics

Thinking Humanly THE COGNITIVE MODELING APPROACH We need to get inside the actual working of human minds Through introspection - trying to catch our own thoughts as they go by Through psychological experiments to have a sufficiently precise theory of the mind COGNITIVE SCIENCE brings together computer models from AI and experimental techniques from psychology

Thinking Rationally THE "LAWS OF THOUGHT" APPROACH Aristotle syllogisms provided patterns for argument structures that always yielded correct conclusions when given correct premises Logicians in the 19th century developed a precise notation for statements about all kinds of things in the world and about the relations among them TWO MAIN OBSTACLES TO THIS APPROACH It is not easy to take informal knowledge and state it in the formal terms required by logical notation There is a big difference between being able to solve a problem "in principle" and doing so in practice

Acting Rationally THE RATIONAL AGENT APPROACH The agent is just something that acts (agents comes from the Latin agere, “to do”) A rational agent is one that acts so as to achieve the best outcome or, when there is uncertainty, the best expected outcome ALL THE SKILLS NEEDED FOR THE TURING TEST ARE THERE TO ALLOW RATIONAL ACTIONS THE STUDY OF AI AS RATIONAL AGENT DESIGN IS MORE GENERAL APPROACH

Two Complementary Views of AI One as an engineering discipline concerned with the creation of intelligent machines One as an empirical science concerned with the computational modeling of human intelligence Former characterizes modern AI, while the later characterizes modern cognitive science

Specialties Which Originated in AI Robotics Pattern Recognition Expert Systems Automatic Theorem Proving Cognitive Psychology Word Processing Machine Vision Knowledge Engineering Computational Linguistics Symbolic Applied Mathematics Intelligent Agent Paradigm Programming Paradigms

Paradigm Shift (1) The science of artificial intelligence from its inception through to the present day is based on the reliance on logic as a way of representing knowledge logical inference (logical reasoning) as the primary mechanism for intelligent reasoning This way of looking at knowledge, language, and thought reflects the rationalist tradition of western philosophy It also reflects the underlying assumptions of Turing test, practically its emphasis on symbolic reasoning, as a test of intelligence, and the belief that a straightforward comparison with human behavior was adequate to confirming machine intelligence

Paradigm Shift (2) The later half of the twentieth century has seen numerous challenges to rationalist philosophy various forms of philosophical relativism question the objective basis of language, science, society, and thought (Wittgenstein’s, Husserl’s, Heidegger’s philosophy; Godel’s and Turing’s views on the very foundations of mathematics) post-modern thought has changed our understanding of meaning and value in the arts and society

Paradigm Shift (3) New (alternative) models of intelligence neural models of intelligence emphasize the brain’s ability to adapt to the world in which it is situated by modifying the relationships between individual neurons work in artificial life and genetic algorithms applies the principles of biological evolution to the problems of finding solutions to difficult problems social systems provide another metaphor for intelligence in that they exhibit global behavior that enable them to solve problems that would confound any of their individual members

Paradigm Shift (4) TWO THEMES First theme is that the view of intelligence is rooted in culture and society, and, as a consequence, emergent Second theme is that intelligence is reflected by the collective behaviors of large number of very simple interacting semi-autonomous individuals, or agents

Paradigm Shift (5) THE MAIN THEMES SUPPORTING AN AGENT-ORIENTED AND EMERGENT VIEW OF INTELLIGENCE Agents are autonomous or semi-autonomous Agents are situated in their environments Agents are interactional (they may be seen as a society) The society of agents is structured (individual agents are coordinated with other agents in the overall problem solving) The phenomenon of intelligence in the environment is emergent (overall cooperative result of the society of agents can be viewed as greater than the sum of its individual contributors)