The phrase "agent memory" is correct and usable in written English.
It can be used in contexts related to artificial intelligence, cognitive science, or computer science, referring to the memory or knowledge that an agent (like a software program or robot) retains.
Example: "The agent memory allows the AI to recall previous interactions and improve its responses over time."
Alternatives: "agent knowledge" or "agent recall".
The agent memory model employed in this article is derived from (Sperling 1967).
This learning starts from the best stored rule set in the agent memory (Experience Bank) thereby decreasing the learning time and creating an intelligent agent with memory.
Although easily done, we specifically do not model mathematically complex, individual agent memory or learning from V ij outcomes as many others do (Axelrod 1987; Sigmund and Nowak 1999).
Once validated, a natural extension is to specifically incorporate agent memory and learning following Quek et al. (2009) and multi-player PD from evolutionary game theory to map the complex transaction networks that emerge over time.
Reference to an object in the agent's current environment is called immediate reference, while reference to cognitive content existing only in the agents' memory, for example, J.S. Bach, is called mediated reference.
The individual agent architecture is hybrid in the sense that the response is reactive but the agent is capable to choose between two different rules in a probabilistic way: one rule depends on the information from the environment and the other depends on the information imprinted in the agents memory.
Our findings raise the possibility that ISRIB or compounds with related activities could serve as invaluable tools in deciphering these higher order brain functions and perhaps be beneficial as a therapeutic agent effecting memory improvement in diseases associated with memory impairment.
Tracking paradigm utilizes the information stored in the current agent's memory and two other important agents, the weighted and best agents, to guide the colony.
Using products means selecting an item from inventory, reducing the stored amount of that item, and then updating the agent's memory about the product usage experience.
Mediated reference, in contrast, relies on language action and recognition (upper level) and the existence of corresponding content in the agent's memory.
In the recognition of colors, the type provided by the agent's memory and the raw input data provided by a sensor interact as follows, resulting in a classified token: A sensor measures the wavelength 470 nm and frequency 640 THz in an agent-external object.
