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# AI: Logic in AI 2

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AI: Logic in AI 2

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### AI: Logic in AI 2

1. 1. Logic in AI 2<br />
2. 2. A simple Planning Agent<br />A simple planning agent uses the percepts provided by the environment to build a complete and correct model of the current world state, after which , to achieve its goal, it calls a suitable planning algorithm (which we will call IDEAL-PLANNER) to generate a plan of action.<br />
3. 3. Problem solving and Planningby a simple planning agent<br />Basic elements of a search-based problem-solver are<br /> Representation of actions,<br /> Representation of states,<br /> Represents of goals and<br /> Representation of plans.<br />
4. 4. Components of practical planning<br />(1) Restrict the language with which PLANNER we define problems. With a restrictivelanguage, there are fewer possible solutions to search through.<br />(2) Use a special-purpose algorithm called a planner rather than a general-purposetheorem prover to search for a solution.<br />
5. 5. Basic representation for planning<br />LEAST COMMITMENT this principle says that one should only make choices about things that you currently working. <br />PARTIAL ORDER A planner that can represent plans in some steps are ordered with respect to each other and other steps are unordered is called a partial order planner.<br />LINEARIZATION A totally ordered plan that is derived from a plan P by adding ordering constraints is called a linearization of P.<br />
6. 6. What is a PLAN?<br />A plan is formally defined as a data structure consisting of the following four components:<br />Set of plan steps<br />Set of step ordering constraints<br />Set of variable binding constraints<br />Set of casual links<br />
7. 7. What is a Solution?<br />A solution is a plan that an agent can execute, and that guarantees achievement of the goal. <br />If we wanted to make it really easy to check that a plan is a solution, we could insist that only fully instantiated, totally ordered plans can be solutions.<br />
8. 8. How to Resolve threats in planning?<br />Resolve now with an equality constraint<br />Resolve now with an inequality constraint<br />Resolve Later<br />
9. 9. Knowledge Engineering for planning<br />Decide what to talk about.<br />Decide on a vocabulary of conditions (literals), operators, and objects.<br />Encode operators for the domain.<br />Encode a description of the specific problem instance.<br />Pose problems to the planner and get back plans.<br />
10. 10. Practical Planning<br />Hierarchical decomposition<br />The practical planners have adopted the idea of hierarchical decomposition: that an abstract operator can be decomposed into a group of steps that forms a plan that implements the operator. <br />These decompositions can be stored in a library of plans and retrieved as needed<br />
11. 11. Analysis of Hierarchical Decomposition<br />Abstract solution A plan that contains abstract operators, but is consistent and complete once an abstract solution is found we can prune away all other abstract plans from the search tree. This property is the downward solution property.<br />We can prune away all the descendants of any inconsistent abstract plan. This is called theupward solution property<br />
12. 12. Resource constraints in planning<br />Using measures in planningThe solution is to introduce numeric-valued measures. Measures such as the price of gas are realities with which the planner must deal, but over which it j has little control. <br />Other measures, such as Cash and Gas Level, are treated as resources that can be produced and consumed.<br />Temporal constraintsIn most ways, time can be treated like any other resource. The initial state specifies a start time for the plan.<br />
13. 13. Visit more self help tutorials<br />Pick a tutorial of your choice and browse through it at your own pace.<br />The tutorials section is free, self-guiding and will not involve any additional support.<br />Visit us at www.dataminingtools.net<br />