Robotics technology

1. Robotics technology Artificial intelligence

2. Artificial Intelligence(AI) • Is the subfield of Computer Science devoted to developing programmes that enable computers to display behaviour that can (broadly) be characterised as intelligent. • Most research in AI is devoted to fairly narrow applications: • planning or • speech-to-speech translation in limited, well defined task domains. • Widespread interest is in the long-range goal of building generally intelligent, autonomous agents.

3. Artificial Intelligence • has been heavily influenced by logical ideas. • has drawn on many research methodologies. • Most members of the AI community would agree that logic has an important role to play in at least some central areas of AI research, and an influential minority considers logic to be the most important factor in developing strategic, fundamental advances.

4. Logic and Artificial Intelligence • Theoretical computer science developed out of logic, the theory of computation (if this is to be considered a different subject from logic), and some related areas of mathematics. • Computer scientists in general are familiar: • with the idea that logic provides techniques for analysing the inferential properties of languages, and • with the distinction between a high-level logical analysis of a reasoning problem and its implementations. • Logic, e.g., can provide a specification for a programming language by characterising a mapping from programmes to the computations that they do. • In computer science, mapping is a logical connection between two entities.

5. Logical theories in AI are independent from implementations. • They can be used to provide insights into the reasoning problem without directly informing the implementation. • Direct implementations of ideas from logic, theorem-proving and model-construction techniques, are used in AI, but the AI theorists who rely on logic to model their problem areas are free to use other implementation techniques as well.

6. Knowledge representation • In response to the need to design the declarative component, a subfield of AI known as knowledge representation emerged during the 1980s. • Knowledge representation deals primarily with the representational and reasoning challenges of this separate component.

7. The importance of applications in logical AI, and the scale of these applications, represents a new methodology for logic - one that would have been impossible without mechanised reasoning. • This methodology forces theoreticians to think through problems on a new scale and at a new level of detail, and this in turn has a profound effect on the resulting theories.

8. The most influential figure in logical AI is John McCarthy (1927 – 2011). • McCarthy, one of the founders of AI, consistently advocated a research methodology that uses logical techniques to formalise the reasoning problems that AI need. • McCarthy's methodological position has not changed substantially since it was first articulated in: McCarthy, John, 1959, “Programs with common sense”, in Proceedings of the Teddington Conference on the Mechanization of Thought Processes, London: Her Majesty's Stationary Office, 75-91, • and elaborated and amended in : McCarthy, John &Patrick J.Hayes, 1969, “Some philosophical problems from the standpoint of artificial intelligence”, in Machine Intelligence 4, Bernard Meltzer & Donald Michie, eds., Edinburgh: Edinburgh University Press, 463-502.

9. The motivation for using logic is : • even if the eventual implementations do not directly and simply use logical reasoning techniques like theorem proving, a logical formalisation helps us to understand the reasoning problem itself. • The claim is that without an understanding of what the reasoning problems are, it will not be possible to implement their solutions. • Plausible as this Platonic argument may seem, it is in fact controversial in the context of AI; an alternative methodology would seek to learn or evolve the desired behaviours. • The representations and reasoning that this methodology would produce might well be too complex to characterise or to understand at a conceptual levels to solve.