Agent platform Tools Types
Different Agent Tools are: 1. JADE 2. SPADE 3. Retisina 4. Jason 5. Jack 6. JIAC, 7. AgentScape, 8. INGENIAS Development Kit 9. SeSAm 10. EMERALD
Different categories of Agent Based Intelligent Systems
1. Coordination2. Integration3. Mobility4. Believable agent5. Assistance
Thank you Babu. Coordination among agents performing interdependent tasks in a distributed problem-solving system is of crucial relevance with respect to the overall performance achieved by the system. Various forms of coordination require communication among agents which may accidentally be distorted.
What’s the difference between strong AI and weak AI?
Strong AI can successfully imitate human intelligence and is at the core of advanced robotics. Weak AI can only predict specific characteristics that resemble human intelligence. Alexa and Siri are excellent examples of weak AI. What do you think? Any examples that you can bring in?
Strong AI has a complex algorithm that helps it act in different situations, while all the actions in weak AIs are pre-programmed by a human. Strong AI-powered machines have a mind of their own. They can process and make independent decisions, while weak AI-based machines can only simulate human behavior.
Model-based reflex agent
The Model-based agent can work in a partially observable environment, and track the situation. Lets discuss more here.
To handle the problem of partial observability, the agent needs to keep track of the world it can’t see. The agent makes a model of the world internally using the previous percepts and thereby reflects some of the unobserved aspects of the current state.
Simple Reflex agent
The Simple reflex agents are the simplest agents. These agents take decisions on the basis of the current precepts and ignore the rest of the precept history.
This is the simplest kind of agent, where the actions are selected based on the current percepts, ignoring the history of percepts. This is very efficient for simple agents like the vacuum-cleaning agent discussed previously.
Goal based agent is one which choose its actions in order to achieve goals. It is a problem solving agent and is more flexible than model reflex agent.Goal based agent consider the future actions. Lets discuss more here
Knowing just the current state is sometimes not enough to decide the actions t be performed. The agent needs some sort of goal information that indicates the desirable states in the environment. It keeps track of the world state as well as a set of goals it’s trying to achieve, and chooses an action that will (eventually) lead to the achievement of its goals.
Its an AI agent in which decision making depends upon the manipulation of data structures representing the beliefs, desires, and intentions of the agent
The belief–desire–intention software model is a software model developed for programming intelligent agents. Superficially characterized by the implementation of an agent's beliefs, desires and intentions, it actually uses these concepts to solve a particular problem in agent programming.
An expert system is an AI software that uses knowledge stored in a knowledge base to solve problems that would usually require a human expert. Any other thoughts?
In artificial intelligence, an expert system is a computer system that emulates the decision-making ability of a human expert. Expert systems are designed to solve complex problems by reasoning through bodies of knowledge, represented mainly as if–then rules rather than through conventional procedural code.
Fuzzy Logic Systems (FLS) produce acceptable but definite output in response to incomplete, ambiguous, distorted. Can somebody give little more clarity on the same. Any thoughts?
Fuzzy logic is an approach to computing based on "degrees of truth" rather than the usual "true or false" (1 or 0) Boolean logic on which the modern computer is based. ... It may help to see fuzzy logic as the way reasoning really works and binary or Boolean logic is simply a special case of it.
Soft computing, as opposed to traditional computing, deals with approximate models and gives solutions to complex real-life problems. Soft Computing techniques are Fuzzy Logic, Neural Network, Support Vector Machines, Evolutionary Computation and Machine Learning and Probabilistic Reasoning.
Soft computing is the use of approximate calculations to provide imprecise but usable solutions to complex computational problems. ... The tolerance of soft computing allows researchers to approach some problems that traditional computing can't process.