# Tag Info

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Let $D_a$ be the domain for A, and $a_i$ the elements of $D_a$. Let $D_b$ and $D_c$ work similarly for $B$ and $C$ respectively. We introduce $D_t = \{t | t = (c_i - b_j, c_i)\}$ for all $c_i$ in $D_c$ and $b_j$ in $D_b$. We can see that $\{t[0]\}$ (i.e. $\{c_i - b_j, \forall i,j\}$) must be equal to $D_a$. So we can represent the constraint on $A$ by ...

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It looks as if 'function' is being used here in the mathematical (or functional programming) sense of 'pure function', i.e. it is without state or side-effects. Hence the function cannot store previous percepts anywhere, so the entire historical percept sequence is considered to be passed to the function each time. In contrast, the notion of 'program' ...

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A stochastic process has the Markov property if the probability distribution of future states conditioned on both the present and past states depends only on the present state or, more formally, the following equality holds. $$p(s_{t+1} \mid s_{t}, s_{t-1:1}) = p(s_{t+1} \mid s_{t}), \forall t$$ The hidden Markov model (HMM) is an example of a model ...

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I would say these terms are often used interchangeably in AI. When they differ, I would say that problem modeling means finding a mathematical description of the problem, while problem representation means finding a particular way to represent that mathematical formalism. For example, a list of numbers can be stored (represented) with a linked list, and ...

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In addition to the books already mentioned, I would like to recommend to you some that helped me understand the basics and guided me through my first AI / CI implementations. Computational Intelligence: An Introduction by Andries P. Engelbrecht It includes the most relevant developments in computational intelligence with good discussions on intelligence ...

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I will first recapitulate the key concepts which you need to know in order to understand the answer to your question (which will be very simple, because I will just try to clarify what is given as a "definition"). In logic, a formula is e.g. $f$, $\lnot f$, $f \land g$, where $f$ can be e.g. the proposition (or variable) "today it will rain". So, in a (...

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A tabular system for agent decisions is a direct and simple map of percept to control choice. For each percept received, the agent looks up the percept and cross-references it to the action it should take. In order to construct this, you need to list all percepts in full detail, with the associated control choice. Clearly that is not going to be feasible for ...

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Firstly, you should be clear about on which subject you want to study. AIMA deals with conventional ai algorithms like path planning, logic etc. Elements of statistical learning is a machine learning book which covers most of the machine learning algorithms you will come across (spare deep learning). digital image processing is an entirely different field ...

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I think this is a problem with missing brackets in pseudocode — clearly the state is only added to the frontier if it hasn't been explored already, so it would be: if not [contains(frontier, state) OR contains(explored, state)] then which is equivalent to your interpretation of if not [contains(frontier, state)] AND not [contains(explored, state)] ...

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What's the difference between the two terms? Don't they mean the same thing? They mean different things, and can occur in any combination. A known, deterministic environment This is an environment where the researcher knows how to calculate all the transitions in advance of observing them, and the transition from state $s$ given action $a$ is always to ...

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You forgot to calculate and take into account the costs of the actual paths. You forgot to accumulate the cost of the edges for going forward and backward multiple times! The evaluation function of uniform-cost search (UCS) is $f(n) = g(n)$, where $g(n)$ represents the cost of the path from the start node to $n$. The evaluation function of A* is \$f(n) = g(n)...

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This is probably more easily understood as the collapse/restore macro. The idea is that the previously explored state was collapsed and only the minimum f-cost from the sub-tree was stored. This represents the best unexpanded state in the subtree that was collapsed. When restoring the portion of the collapsed tree, the f-cost of the restored node could ...

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Initial state: initial position of the monkey. Possible actions climb on the crate, get down the crate, move the crate from one spot to another, stack one crate on another, walk from one spot to another, grab bananas (if standing on the crate) Goal test: did the monkey get the bananas? Cost function: the number of actions completed

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