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…
Polyglot Programming & Paradigm Shifting draws the line between logic programming and goal-oriented programming precisely: one answers "is this true?", the other answers "what sequence of actions gets me from here to a…
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…