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multiple valued logicsmultiple valued logics where altered types of truth value such as unknown are may be allowed these have some of the particular
fuzzy logicin the logics we are here described above what we have been concerned with truth whether propositions and sentences are true moreover with
higher order predicate logic in the very first order predicate logic we are only allowed to quantify over objects if we are considered to allow
first order predicate logic this is a more expressive logic because it is mostly builds on propositional logic by allowing us to needs as constants
propositional logicthis is a fairly restrictive logic that allows us to be write sentences about notpropositions - statements about the world -
logical representationsif all human beings spoke the same language there would be a much more less misunderstanding in the world the problem
knowledge representationto recap we now have some characterizations of ai that when an ai problem arises you will be able to put all into context
assessing heuristic searchesgiven a particular problem you want to build an agent to solve so there may be more than one way of justifying it as a
common problem with hill climbingan alternative way of justifying the problem is that the states are boards with 8 queens already on them so an
hill climbing - artificial intelligenceas weve seen in some problems finding the search path from primary to goal state is the point of the exercise
random search - artificial intelligencesome problems to be solved by a search agent are more creative in nature for example like in writing poetry in
simulated annealingone way to get around the problem of local maxima and related problems like ridges and plateaux in hill climbing is to allow the
ida search - artificial intelligencea search is a sophisticated and successful search strategy in fact a problem with a search is that it must keep
a search - artificial intelligencea search in the combines is the best parts of uniform cost search namely the fact that its optimal and complete and
greedy search - artificial intelligenceif we have a heuristic function for states defined as above so we can simply measure each state with respect
uniform path cost search - artificial intelligencea breadth first search will find the solution with the shortest path length from the initial state
optimality - heuristic search strategiesthe path cost of a solution is considered as the sum of the costs of the actions that led to which solution
heuristic search strategiesgenerally speaking that a heuristic search is one which have uses a rule of thumb to improve an agents performance in
iterative deepening searchso breadth first search is always guaranteed to find a solution if one exists actually it eats all the memory for the depth
advantage to depth first searchit just looks like it will be a long period it finds dan until this highlights an important drawback for depth first
depth first search - artificial intelligencedepth first search is very similar to breadth first except for that the things are added to the top of
process of breadth first searchits very useful to think of this search as the evolution of the given tree and how each string of letters of word is
breadth first searchgiven a set of operators o1 on in a breadth first search every time a new state is reached an action for each operator on
different search strategies- artificial intelligenceto help us think about the different search strategies we use two analogies firstly we suppose
uninformed search strategiesto be able to undertake an unaware search there is really important that the entire agent needs to know is the primary