AI News, Difference between revisions of "Category:Artificial intelligence"
- On Thursday, October 4, 2018
- By Read More
Difference between revisions of "Category:Artificial intelligence"
Anyone impressed by the increasingly dazzling speed and colossal memory capacity of computers will not be able to find in these or any other astonishing computer traits any manifestation of the slightest fragment of intelligence as long as computer programming remains purely algorithmic.
An algorithm enables the computer to repeat long suites of logical operations tirelessly and accurately, as long as the algorithm is correct.
The following example helps to illustrate the problem with algorithmic programming: you cannot ask a financial expert to predict all of the events that may occur during a year, a month, or even a day.
This is also true of programs based on artificial intelligence, expert systems, fuzzy logic, neural networks, object-oriented languages, and so on.
For instance, an algorithm might be designed to play chess, but the complexity of the game makes it unusable, because for a computer to win each time, the course of action would last for centuries.
However, chess is child's play compared to analyzing stock-market fluctuations or the pattern of air vortices over an aircraft's wing.
In other words, the addition of a new agent corresponds to a new competence and the addition of a new agent does not imply the modification of a principal program: each agent autoconstructs its own interpreter. An
Mindsuite’s Goal-oriented agents have the ability to determine what kind of information is good or bad or in favor or disfavor with one of the goals (global or local).
What difference is there between a good game of electronic chess (programmed with all of the known algorithmic techniques like MinMax, ab, scout, and ss*) and an excellent player?
The computer works in brute force, working at a prodigious speed to best react to the present situation and the situations that might occur during the next five or six moves.
For example, a human player might sacrifice a pawn to save a knight, preserving the knight and fooling the computer and distracting it by this defensive strategy.
The player progressively puts the conditions of the win in place of this objective in working out his or her strategy in goals and subgoals.
With a nonalgorithmic technology, it is possible to overcome the limits of classic technologies in that you can assign goals to your agents without any need for programming, consequently going beyond the algorithmic techniques.
Smart multiagent solution consists of a group of agents, each one with an expertise that communicates among one another, researching the equilibrium of everything--identical to a human society.
however, it surpasses the limits of object languages: It enables the creation of applications even when the algorithm is unknown, or its complexity makes it useless. What
Until now, the resolution of any computer problem has been to find the best way to move from an initial state to a final state by exploring intermediary states.
In fact, the majority of complex programs using classic techniques: object-oriented languages, expert systems, and the classic algorithms are often faced with the problem of combinatorial explosion, intrinsic to the philosophy of the exploration of states.
It represents everything that the agent considers true, generally the information is values taken by agents, attributes, or even attributes of agents.
The concept of an environment has the same functionalities as the theory of beliefs, although being less philosophical than a theory of beliefs and much simpler to use.
The information stocked in the agent's environment can have a temporary validity (contain an amount of validity), which gives the system the possibility to proceed to a temporal collecting of garbage.
If an agent discovers an interesting piece of information in the public zone of its environment, it can at any moment transfer this information to its private zone, after having validated this information. 1.3
That equates to a request for information from the agent 'road,' which is going to respond by placing in the private zone of the requesting agent's environment the information 'traffic jam.'
This information will be translated in the public zone of the agent's environment, free from the receiving agent in order to understand it, while making the information pass from the public zone to the private zone. 2.
Very close to the technique of neural networks, they add the auto reflexivity to each neuron (this technique does not exist in neural techniques): in regards to the individual and combined force of these information streams, the agent may or may not generate an exit signal.
Take a credit-card transaction for example: the input is the order placed and the three following outputs are the picking slip, the credit card authorization, and the shipping order.
If you wish to associate a demand function to each agent, you must assign a method attribute with a previously fixed name.
Once the propagation has taken place, a particular action that calls on a method attribute and that sends the obtained result is defined as a user function ':func.' 2.2
The specification for this minisystem will define four prerequisite agents: fire, smoke detector, temperature detector, and radiation detector.
Therefore, we add agents danger personnel, danger material, danger central, and rapid intervention to the system.
With the classic programming techniques (C++, C, etc.), object languages, etc., the treatment corresponds to: Exchange of classic messages between three agents.
Here, thanks to the continuations, an agent has the possibility to send a message and to require that the result be directly continued toward one or several other agents, thus considerably reducing the number of messages exchanged. 4.
To resolve them, individuals generally work in a group, putting their diverse knowledge together and collaborating toward a common objective.
For example, in any given company, on any given problem, differing points of view, with contradictions, can arise between the head of the technical department, the director of marketing, and others while they each work for the good of the company.
For example, you can use a system that renders information (similar to the Windows menu) useless or forbidden to the speaker, or even that depends on a precise goal. 6.
Even with a single-processor computer, two or more agents can 'move at the same time,' meaning that the election of an agent at a given moment cannot affect the behavior of another agent until the next cycle.
The conception of each agent can be performed independently of the others, since each agent only affects others by the fact that they are in favor or disfavor of one of the goals of an agent. 9.
With a nonalgorithmic agent, the message is no longer a passive request, but, on the contrary, knowledge whose importance varies.
system works in real time if it is capable of absorbing all of the entered information, if it is not too old for the interest it presents, and by reacting to the information quickly enough so that the reaction has meaning.
Soft: In soft real-time systems the response times are important, but the system will continue to function even if certain time limits have elapsed.
The notion of real time requires the system to guarantee a calculated time inferior to a certain period.
In this way, according to the imperative period, agents can modify the impact of the tasks (less or more) or even propose the best solution that is compatible with the deadline. 18.
The manipulation of imprecise and incomplete notions and a nuance in reasoning allow for an approximate evaluation of information on the agents rather than a strict binary response. 20.
Because of its local vision, an agent is to carry out evaluations on two levels: (1) on the scale of its own data (this evaluation can only be carried out by the agent and is constituted of the weakest judgment of the data;
An agent's evaluation reflects the exact vision of the agent based on its own knowledge and partial vision of the entire outside world. 22.
- On Wednesday, June 26, 2019
Lesson Code: Learn how to detect the user browser software ..
Actor Critic Algorithms
Reinforcement learning is hot right now! Policy gradients and deep q learning can only get us so far, but what if we used two networks to help train and AI ...
Lecture 16 | Adversarial Examples and Adversarial Training
In Lecture 16, guest lecturer Ian Goodfellow discusses adversarial examples in deep learning. We discuss why deep networks and other machine learning ...
GALA demo - A BML-based Embodied Conversational Agent for a Personality Detection Program.wmv
Short video demo of the continuation of my Thesis, for the GALA event. For more information about the Event :
Modelling interacting humans by agent-based social simulation
UbikSim is an agent-based social simulator conceived for evaluating Ambient Intelligence systems where direct testing is infeasible due to the large number of ...
Intrusion Detection (IDS) Best Practices
Learn the top intrusion detection best practices. In network security no other tool is as valuable as intrusion detection. The ability to locate and identify malicious ...
Learning to Communicate with Deep Multi Agent Reinforcement Learning
Jakob N. Foerster, Yannis M. Assael, Nando de Freitas, Shimon Whiteson Barcelona, NIPS 2016 [ ..
IQA: Visual Question Answering in Interactive Environments
We introduce Interactive Question Answering (IQA), the task of answering questions that require an autonomous agent to interact with a dynamic visual ...
How to Improve Security with AlienVault OTX Threat Data
With the constant evolution of today's threat landscape, it is becoming more and more challenging for mid-market organizations to detect attacks simply because ...
Preliminary Comments: AnyLogic Spatial Environments [Agent-Based Modeling for Health Policy]
Syllabus page with additional information is available at Lectures on ..