Theory and practice in designing and implementing cognitive systems
Every system this module studies — a thermostat, a chess program, a warehouse robot, a large language model calling tools on its own behalf — shares one property: it sits inside an environment, takes in information…
The foundational cognitive cycle page set out a full model: ten components, densely cross-connected, more than most real systems ever need. This page asks a more practical question — starting from almost nothing and…
Before you build an agent, you need to specify what it actually is — and it turns out two entirely separate disciplines, working on entirely separate problems, converged on the same first move: force the specification…
The cognitive cycle names Think as a distinct stage without saying how it's actually implemented. This page covers the two fundamentally different answers computer science has produced, using two real, contrasting…
Every page in this module so far has treated an agent as a single thing reasoning about a world. Almost every real deployment involves more than one. This page covers how separate agents actually exchange information…
Every capability this module has covered comes with a corresponding failure mode, and the failure modes are worth studying as carefully as the capabilities, because they don't announce themselves. A brittle agent, a…
The previous page named three ways an agent fails without announcing it. This page covers concrete design responses — not a way to make these failures impossible, since several of them are structural rather than…
The multi-agent systems page covers several mechanisms agents use to exchange information, from direct signals to shared resources to formal communication languages. This page asks a narrower question about all of them…
This site's material on AI Ethics covers bias, fairness, and the regulatory landscape for AI systems broadly. This page narrows the focus to a problem specific to autonomous action rather than classification or…