Software agents in ai

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Software agents in ai

An AI system can be defined as the study of the rational agent and its environment. The agents sense the environment through sensors and act on their environment through actuators.

An AI agent can have mental properties such as knowledge, belief, intention, etc. An agent can be anything that perceiveits environment through sensors and act upon that environment through actuators. An Agent runs in the cycle of perceivingthinkingand acting.

An agent can be:. Hence the world around us is full of agents such as thermostat, cellphone, camera, and even we are also agents.

Sensor: Sensor is a device which detects the change in the environment and sends the information to other electronic devices. An agent observes its environment through sensors. Actuators: Actuators are the component of machines that converts energy into motion. The actuators are only responsible for moving and controlling a system.

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An actuator can be an electric motor, gears, rails, etc. Effectors: Effectors are the devices which affect the environment. Effectors can be legs, wheels, arms, fingers, wings, fins, and display screen. An intelligent agent is an autonomous entity which act upon an environment using sensors and actuators for achieving goals.

An intelligent agent may learn from the environment to achieve their goals. A thermostat is an example of an intelligent agent.

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A rational agent is an agent which has clear preference, models uncertainty, and acts in a way to maximize its performance measure with all possible actions. A rational agent is said to perform the right things.

software agents in ai

AI is about creating rational agents to use for game theory and decision theory for various real-world scenarios. For an AI agent, the rational action is most important because in AI reinforcement learning algorithm, for each best possible action, agent gets the positive reward and for each wrong action, an agent gets a negative reward.

The rationality of an agent is measured by its performance measure. Rationality can be judged on the basis of following points:. The task of AI is to design an agent program which implements the agent function. The structure of an intelligent agent is a combination of architecture and agent program.

It can be viewed as:.A software agent is an piece of software that functions as an agent for a user or another program, working autonomously and continuously in a particular environment.

It is inhibited by other processes and agents, but is also able to learn from its experience in functioning in an environment over a long period of time.

Software agents offer various benefits to end users by automating repetitive tasks. The basic concepts related to software agents are:. Toggle navigation Menu. Home Dictionary Tags Software. Software Agent Last Updated: August 18, Definition - What does Software Agent mean? Techopedia explains Software Agent Software agents offer various benefits to end users by automating repetitive tasks. The basic concepts related to software agents are: They are invoked for a task They reside in "wait" status on hosts They do not require user interaction They run status on hosts upon starting conditions They invoke other tasks including communication There are a number of different software agents, including: Buyer Agents or Shopping Bots: These agents revolve around retrieving network information related to good and services.

User or Personal Agents: These agents perform a variety of tasks such as filling out forms, acting as opponents in games, assembling customized reports and checking email, among other tasks. Monitoring and Surveillance Agents: These agents observe and report on equipment.

Data-Mining Agents: These agents find trends and patterns in many different sources and allow users to sort through the data to find the information they are seeking. Share this:. Related Terms. Related Articles. The Laws of Computing. Should You Always Aspire to be Agile? What is the difference between a mobile OS and a computer OS? What is the difference between alpha testing and beta testing?

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What components make up an IT infrastructure, and how do they work together? More of your questions answered by our Experts. Related Tags. Machine Learning and Why It Matters:. Latest Articles. How Cryptomining Malware is Dominating Cybersecurity.Artificial intelligence is defined as a study of rational agents. A rational agent could be anything which makes decisions, as a person, firm, machine, or software. An AI system is composed of an agent and its environment.

The agents act in their environment. The environment may contain other agents. An agent is anything that can be viewed as :.

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Note : Every agent can perceive its own actions but not always the effects To understand the structure of Intelligent Agents, we should be familiar with Architecture and Agent Program.

Architecture is the machinery that the agent executes on. It is a device with sensors and actuators, for example : a robotic car, a camera, a PC. Agent program is an implementation of an agent function. An agent function is a map from the percept sequence history of all that an agent has perceived till date to an action.

Examples of Agent:- A software agent has Keystrokes, file contents, received network packages which act as sensors and displays on the screen, files, sent network packets acting as actuators.

A Human agent has eyes, ears, and other organs which act as sensors and hands, legs, mouth, and other body parts acting as actuators. A Robotic agent has Cameras and infrared range finders which act as sensors and various motors acting as actuators. Agents can be grouped into four classes based on their degree of perceived intelligence and capability :.

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Simple reflex agents ignore the rest of the percept history and act only on the basis of the current percept. Percept history is the history of all that an agent has perceived till date. The agent function is based on the condition-action rule.

A condition-action rule is a rule that maps a state i. If the condition is true, then the action is taken, else not. This agent function only succeeds when the environment is fully observable. For simple reflex agents operating in partially observable environments, infinite loops are often unavoidable.

It may be possible to escape from infinite loops if the agent can randomize its actions. Problems with Simple reflex agents are :. It works by finding a rule whose condition matches the current situation. A model-based agent can handle partially observable environments by use of model about the world. The agent has to keep track of internal state which is adjusted by each percept and that depends on the percept history. The current state is stored inside the agent which maintains some kind of structure describing the part of the world which cannot be seen.

Updating the state requires information about :. These kind of agents take decision based on how far they are currently from their goal description of desirable situations. Their every action is intended to reduce its distance from the goal. This allows the agent a way to choose among multiple possibilities, selecting the one which reaches a goal state. The knowledge that supports its decisions is represented explicitly and can be modified, which makes these agents more flexible.

They usually require search and planning. The agents which are developed having their end uses as building blocks are called utility based agents. When there are multiple possible alternatives, then to decide which one is best, utility-based agents are used.

They choose actions based on a preference utility for each state. Sometimes achieving the desired goal is not enough. We may look for a quicker, safer, cheaper trip to reach a destination.

Agent happiness should be taken into consideration.An AI system is composed of an agent and its environment. The agents act in their environment. The environment may contain other agents. An agent is anything that can perceive its environment through sensors and acts upon that environment through effectors.

A human agent has sensory organs such as eyes, ears, nose, tongue and skin parallel to the sensors, and other organs such as hands, legs, mouth, for effectors. A robotic agent replaces cameras and infrared range finders for the sensors, and various motors and actuators for effectors.

Rationality is nothing but status of being reasonable, sensible, and having good sense of judgment.

software agents in ai

Rationality is concerned with expected actions and results depending upon what the agent has perceived. Performing actions with the aim of obtaining useful information is an important part of rationality. A rational agent always performs right action, where the right action means the action that causes the agent to be most successful in the given percept sequence. They choose their actions in order to achieve goals.

Goal-based approach is more flexible than reflex agent since the knowledge supporting a decision is explicitly modeled, thereby allowing for modifications. Goals have some uncertainty of being achieved and you need to weigh likelihood of success against the importance of a goal.

Some programs operate in the entirely artificial environment confined to keyboard input, database, computer file systems and character output on a screen. In contrast, some software agents software robots or softbots exist in rich, unlimited softbots domains.

AI - Agents & Environments

The simulator has a very detailed, complex environment. The software agent needs to choose from a long array of actions in real time. A softbot designed to scan the online preferences of the customer and show interesting items to the customer works in the real as well as an artificial environment. The most famous artificial environment is the Turing Test environmentin which one real and other artificial agents are tested on equal ground. This is a very challenging environment as it is highly difficult for a software agent to perform as well as a human.

Two persons and a machine to be evaluated participate in the test. Out of the two persons, one plays the role of the tester.

Each of them sits in different rooms. The tester is unaware of who is machine and who is a human. He interrogates the questions by typing and sending them to both intelligences, to which he receives typed responses. This test aims at fooling the tester. The quality of its action depends just on the episode itself.As illustrated in the following example, it's possible to provide a list of input fields, selecting the fields from the filtered input dataset that will be created.

Filtering happens before field picking and, therefore, the row filter can use fields that won't end up in the cloned dataset. See the Section on filtering sources for more details. Each new field is created using a Flatline expression and optionally a name, label, and description. A Flatline expression is a lisp-like expresion that allows you to make references and process columns and rows of the origin dataset. See the full Flatline reference here. Let's see a first example that clones a dataset and adds a new field named "Celsius" to it using an expression that converts the values from the "Fahrenheit" field to Celsius.

A new field can actually generate multiple fields. In that case their names can be specified using the names arguments. In addition to horizontally selecting different fields in the same row, you can keep the field fixed and select vertical windows of its value, via the window and related operators. For example, the following request will generate a new field using a sliding window of 7 values for the field named "Fahrenheit" and will also generate two additional fields named "Yesterday" and "Tomorrow" with the previous and next value of the current row for the field 0.

The list of values generated from each input row that way constitutes an output row of the generated dataset. See the table below for more details. See the Section on filtering rows for more details. Example: "description": "This field is a transformation" descriptions optional Array A description for every of the new fields generated.

Example: "fields": "(window Price -2 0)" label optional Array Label of the new field. Example: "label": "New price" labels Array Labels for each of the new fields generated. Example: "name": "Price" names optional Array Names for each of the new fields generated.

Software Agents

Basically, a Flatline expresion can easily be translated to its json-like variant and vice versa by just changing parentheses to brackets, symbols to quoted strings, and adding commas to separate each sub-expression.

For example, the following two expressions are the same for BigML. If you specify both sampling and filtering arguments, the former are applied first. As with filters applied to datasources, dataset filters can use the full Flatline language to specify the boolean expression to use when sifting the input. Flatline performs type inference, and will in general figure out the proper optype for the generated fields, which are subsequently summarized by the dataset creation process, reaching then their final datatype (just as with a regular dataset created from a datasource).

Agents in Artificial Intelligence

In case you need to fine-tune Flatline's inferences, you can provide an optype (or optypes) key and value in the corresponding output field entry (together with generator and names), but in general this shouldn't be needed. Samples Last Updated: Monday, 2017-10-30 10:31 A sample provides fast-access to the raw data of a dataset on an on-demand basis. When a new sample is requested, a copy of the dataset is stored in a special format in an in-memory cache.

Multiple and different samples of the data can then be extracted using HTTPS parameterized requests by sampling sizes and simple query string filters. That is to say, a sample will be available as long as GETs are requested within periods smaller than a pre-established TTL (Time to Live).Great customer service doesn't hurt either.

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This app is part of a suite of apps that really make it easy to run an eBusiness. It offers one place to view all the or. I use it all the time highly recommended :)First of all.You can use BigML. That is to say, using BigML. Fully white-box access to your datasets, models, clusters and anomaly detectors. Asynchronous creation of resources.

The four original BigML resources are: source, dataset, model, and prediction. As shown in the picture below, the most basic flow consists of using some local (or remote) training data to create a source, then using the source to create a dataset, later using the dataset to create a model, and, finally, using the model and new input data to create a prediction. The training data is usually in tabular format.

Each row in the data represents an instance (or example) and each column a field (or attribute). These fields are also known as predictors or covariates. When the machine learning task to learn from training data is supervised one of the columns (usually the last column) represents a special attribute known as objective field (or target) that assigns a label (or class) to each instance. The training data in this format is named labeled and the machine learning task to learn from is named supervised learning.

Once a source is created, it can be used to create multiple datasets. Likewise, a dataset can be used to create multiple models and a model can be used to create multiple predictions. A model can be either a classification or a regression model depending on whether the objective field is respectively categorical or numeric.

Often an ensemble (or collection of models) can perform better than just a single model. Thus, a dataset can also be used to create an ensemble instead of a single model. A dataset can also be used to create a cluster or an anomaly detector. Clusters and Anomaly Detectors are both built using unsupervised learning and therefore an objective field is not needed.

In these cases, the training data is named unlabeled.

software agents in ai

A centroid is to a cluster what a prediction is to a model. Likewise, an anomaly score is to an anomaly detector what a prediction is to a model. There are scenarios where generating predictions for a relative big collection of input data is very convenient. For these scenarios, BigML. These resources take a dataset and respectively a model (or ensemble), a cluster, or an anomaly detector to create a new dataset that contains a new column with the corresponding prediction, centroid or anomaly score computed for each instance in the dataset.

Note: In the snippets below you should substitute Alfred's username and API key for your own username and API Key. You can create, read, update, and delete resources using the respective standard HTTP methods: POST, GET, PUT and DELETE. All communication with BigML. All access to BigML. In this way communication between your application and BigML. In development mode, you do not consume any credits and you cannot create datasets that are bigger than 16 MB each even if you can create sources of any size.

If you omit the version name in your API requests, you will always get access to the latest API version. While we will do our best to make future API versions backward compatible it is possible that a future API release could cause your application to fail. Operation HTTP method Semantics CREATE POST Creates a new resource.

Only certain fields are "postable". This method is not idempotent. Each valid POST request results in a new directly accessible resource.

software agents in ai

RETRIEVE GET Retrieves either a specific resource or a list of resources.


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