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Tuesday, February 20, 2024

Springer publication -Q1 journal

I am thrilled to share the exciting news that my paper has been selected for publication in Heritage Science. The paper, titled "Comparative study: enhancing legibility of ancient Indian script images from diverse stone background structures using 34 different pre-processing methods ," explores automation of ancient script. I am grateful for the opportunity to contribute to the field and engage with the heritage science community. The article is now available online, and you can access it via the following link: https://rdcu.be/dy8WF DOI link: https://lnkd.in/d_7BdCBY The impact factor of HERITAGE SCIENCE is 2.843. The HERITAGE SCIENCE is a reputed research journal. It is published by Springer Science + Business Media. The journal is indexed in Scopus. #springer #heritagepreservation #annauniversity #scopusindexed #nature #researchpaper #imageprocessing #impact

Topic 5: Agent Types

Agent Types :

Table-driven agent
Simple reflex agent
Reflex agent with internal state
Agent with explicit goals
Utility-based agent

Simple Reflex Agents: Reacting Swiftly to the Present

Model-Based Agents: Planning for the Future

Goal-Based Agents: Working Towards Objectives

Utility-Based Agents: Balancing Preferences and Trade-offs

Learning Agents: Adapting and Improving Over Time

(1) Table-driven agents

use a percept sequence/action table in memory to find the next action. They are implemented by a (large) lookup table.

(2) Simple reflex agents

are based on condition-action rules, implemented with an appropriate production system. They are stateless devices which do not have memory of past world states.

(3) Agents with memory - Model-based reflex agents

have internal state, which is used to keep track of past states of the world.

(4) Agents with goals – Goal-based agents

are agents that, in addition to state information, have goal information that describes desirable situations. Agents of this kind take future events into consideration.

(5) Utility-based agents

base their decisions on classic axiomatic utility theory in order to act rationally.

(6) Learning agents

they have the ability to improve performance through learning.

I) --- Table-lookup driven agents

Uses a percept sequence / action table in memory to

find the next action. Implemented as a (large) lookup table.

Drawbacks:

Huge table (often simply too large)

Takes a long time to build/learn the table