Introduction to Reasoning Agent Architectures Deductive Reasoning Agents Practical Reasoning Agents CM30174 Introduction to Intelligent Agents Semester 1, 2010-11 Marina De Vos, Julian Padget Reasoning Agents / 20101011 / version 0.5 October 25, 2010 De Vos/Padget (Bath/CS) CM30174/Reasoning Agents October 25, 2010 1 / 88 Introduction to Reasoning Agent Architectures Deductive Reasoning Agents Practical Reasoning Agents Authors/Credits for this lecture Primary author: Marina De Vos. Material sourced from Michael Wooldridge’s book “An Introduction to Multiagent Systems”, Chapter 3 and 4. [Wooldridge, 2002]. De Vos/Padget (Bath/CS) CM30174/Reasoning Agents October 25, 2010 2 / 88 Introduction to Reasoning Agent Architectures Deductive Reasoning Agents Practical Reasoning Agents Content Introduction to Reasoning 1 Agent Architectures 2 Deductive Reasoning Agents 3 4 Practical Reasoning Agents De Vos/Padget (Bath/CS) CM30174/Reasoning Agents October 25, 2010 3 / 88
Introduction to Reasoning Key Distinctions Agent Architectures Concrete Approaches Deductive Reasoning Agents Summary Practical Reasoning Agents What is Reasoning? More than thinking Taking a set of facts and deriving new ones in a fixed way More specifically (usefully): Reasoning to achieve a goal - planning Problem Solving Working out how to get world state A to world state B De Vos/Padget (Bath/CS) CM30174/Reasoning Agents October 25, 2010 5 / 88 Introduction to Reasoning Key Distinctions Agent Architectures Concrete Approaches Deductive Reasoning Agents Summary Practical Reasoning Agents Examples Samples: I have 7 apples and then give 3 to Vladimir: how many apples do I have? How do I get from San Jose to Puerto Viejo? How do I pass this unit with the least effort? Should I take this unit or not? How do achieve my dream of owning a house by the seaside? Each of these cases has different facts or world conditions Each of these cases requires a different inference mechanism De Vos/Padget (Bath/CS) CM30174/Reasoning Agents October 25, 2010 6 / 88 Introduction to Reasoning Key Distinctions Agent Architectures Concrete Approaches Deductive Reasoning Agents Summary Practical Reasoning Agents The Dream House Take the house building problem: Starting world state: I have X amount of money I have many facts about land, the city, planning permission, the housing market etc. How do I achieve my goal state: Where I have a house (preferably one which is the BEST I could get with my money) The possibilities in the real world are (nearly!) infinite! De Vos/Padget (Bath/CS) CM30174/Reasoning Agents October 25, 2010 7 / 88
Introduction to Reasoning Key Distinctions Agent Architectures Concrete Approaches Deductive Reasoning Agents Summary Practical Reasoning Agents Automated Reasoning Objective: carry out such inference automatically - without the need for human intervention This is very hard because: The real world is complex (huge number of factors) inaccessible Resources are bounded (finite time and finite memory) Things change (while I am thinking or acting the world may change) dynamic The world is uncertain (I cannot be sure that an action I take will have the expected outcome) non-deterministica There are other actors (that might try to intentionally or unintentionally thwart my plans!) non-deterministic De Vos/Padget (Bath/CS) CM30174/Reasoning Agents October 25, 2010 8 / 88 Introduction to Reasoning Key Distinctions Agent Architectures Concrete Approaches Deductive Reasoning Agents Summary Practical Reasoning Agents Formulating the Problem Decide on an abstraction / simplification: A model of the world - an abstract vision of the concepts relevant to the problem What to consider / ignore (uncertainty? other agents? ...) Whether or not we are reasoning in one agent or across many (do I have to negotiate?) Each of these affect the complexity of the reasoner De Vos/Padget (Bath/CS) CM30174/Reasoning Agents October 25, 2010 9 / 88 Introduction to Reasoning Key Distinctions Agent Architectures Concrete Approaches Deductive Reasoning Agents Summary Practical Reasoning Agents Monotonic vs. Non-Monotonic (I) Monotonic A logical inference relation is monotonic if and only if, for all sets of propositions S and T , and for all propositions A , if S entails A (e.g. S ⊢ A ) then ( S ∪ T ) ⊢ A First order logic is monotonic Classical deduction - suitable for reasoning in open-ended situations Absence of x implies x is unknown A proposition A is false with respect to a set of propositions S when S ⊢ ¬ A . De Vos/Padget (Bath/CS) CM30174/Reasoning Agents October 25, 2010 10 / 88
Introduction to Reasoning Key Distinctions Agent Architectures Concrete Approaches Deductive Reasoning Agents Summary Practical Reasoning Agents Monotonic vs. Non-Monotonic (II) Non-monotonic Absence of x implies x is false - closed world assumption - Clark’s completion Prolog is non-monotonic Logics in which the set of implications determined by a given group of premises does not necessarily grow, and can shrink, when new well-formed formulae are added to the set of premises Reasoning to conclusions on the basis of incomplete information. Given more information, we are prepared to retract previously drawn inferences. Agents are in general non-monotonic systems. De Vos/Padget (Bath/CS) CM30174/Reasoning Agents October 25, 2010 11 / 88 Introduction to Reasoning Key Distinctions Agent Architectures Concrete Approaches Deductive Reasoning Agents Summary Practical Reasoning Agents Abductive vs. Deductive Abductive A form of inference that works forward to the best explanation Example: D is a collection of data (facts, observations, givens), H explains D (or would, if true, explain D), No other hypothesis explains D as well as H does. Therefore, H is probably correct. Good for diagnosis, plan recognition, natural language understanding, vision Explanation is not necessarily true De Vos/Padget (Bath/CS) CM30174/Reasoning Agents October 25, 2010 12 / 88 Introduction to Reasoning Key Distinctions Agent Architectures Concrete Approaches Deductive Reasoning Agents Summary Practical Reasoning Agents Abductive vs. Deductive Deductive Predictive Works forward from premises to conclusion Inference rules drive the process Uses the existence of facts to infer (via rules) the existence of new facts Conclusion is proven with respect to available facts De Vos/Padget (Bath/CS) CM30174/Reasoning Agents October 25, 2010 13 / 88
Introduction to Reasoning Key Distinctions Agent Architectures Concrete Approaches Deductive Reasoning Agents Summary Practical Reasoning Agents Forward vs. Backward Chaining Forward Effectively, an implementation of deduction Rules are used to deduce new facts from existing facts Process continues until no more rules apply Backward Works backwards from goal to current situation Rules are used to infer that a (sub)goal holds then the preconditions (left hand side of rule) also hold Process moves backwards down chain of reasoning until no more rules apply Prolog style De Vos/Padget (Bath/CS) CM30174/Reasoning Agents October 25, 2010 14 / 88 Introduction to Reasoning Key Distinctions Agent Architectures Concrete Approaches Deductive Reasoning Agents Summary Practical Reasoning Agents Essential in all of these You have a description of the world and a specification of the goal You have a (possibly vast) search space of things to do You traverse the search space in some way De Vos/Padget (Bath/CS) CM30174/Reasoning Agents October 25, 2010 15 / 88 Introduction to Reasoning Key Distinctions Agent Architectures Concrete Approaches Deductive Reasoning Agents Summary Practical Reasoning Agents Concrete Approaches Approaches Case Based Reasoning Model Based Reasoning Qualitative Reasoning Planning Systems Constraint Satisfaction Reasoning Rule Based Reasoning – RBS Ontology Inference, e.g. RACER/FaCT Symbolic Reasoning Logic Programming Note: these are necessarily disjoint. For example there are constraint logic based planning systems Skip Approaches De Vos/Padget (Bath/CS) CM30174/Reasoning Agents October 25, 2010 16 / 88
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