This is the second post in a sequence. For the introduction post, see here.

Graphical World Models

A world model is a mathematical model of a particular world. This can be our real world, or an imaginary world. To make a mathematical model into a model of a particular world, we need to specify how some of the variables in the model relate to observable phenomena in that world.

We introduce our graphical notation for building world models by creating an example graphical model of a game world. In the game world, a simple game of dice is being played. The player throws a green die and a red die, and then computes their score by adding the two numbers thrown.

We create the graphical game world model in thee steps:

1. We introduce three random variables and relate them to observations we can make when the game is played once in the game world. The variable $X$ represents the observed number of the green die, $Y$ is the red die, and $S$ is the score.

2. We draw a diagram:

3. We define the two functions that appear in the annotations above the nodes in the diagram: $$\begin{array}{c}D\left(d\right)=(ifd\in \{1,2,3,4,5,6\left\}then1/6else0\right),\\ sum(a,b)=a+b.\end{array}$$

Informal interpretation of the graphical model

We can read the above graphical model as a description of how we might build a game world simulator, a computer program that generates random examples of game play. To compute one run of the game, the simulator would traverse the diagram, writing an appropriate observed value into each node, as determined by the function written above the node. Here are three possible simulator runs:

We can interpret the mathematical expression $P(S=12)$, the probability that $S$ equals 12, as being the exact probability that the next simulator run puts the number 12 into node $S$.

We can interpret the expression $E\left(S\right)$, the expected value of $S$, as the average of the values that the simulator will put into $S$, averaged over an infinite number of runs.

The similarity between what happens in the above drawings and what happens in a spreadsheet calculation is not entirely coincidental. Spreadsheets can be used to create models and simulations without having to write a full computer program from scratch.

Formal interpretation of the graphical model

In section 2.4 of the paper, I define the exact formal semantics of graphical world models. These formal definitions allow one to calculate the exact value of $P(S=12)$ and $E\left(S\right)$ without running a simulator.

Relation between the model and the world

A mathematical model can be used as a theory about a world, but it can also be used as a specification of how certain entities in that world are supposed to behave. If the model is a theory of the game world, and we observe the outcome $X=1,Y=1,S=12$, then this observation falsifies the theory. But if the model is a specification of the game, then the same observation implies that the player is doing it wrong.

In the AGI alignment community, the agent models that are being used in the mainstream machine learning community are sometimes criticized for being too limited. It we read such a model as a theory about how the agent is embedded into the real world, this theory is obviously flawed. A real live agent might modify its own compute core, changing its build-in policy function. But in a typical agent model, the policy function is an immutable mathematical object, which cannot be modified by any of the agent's actions.

If we read such an agent model instead as a specification, the above criticism about its limitations does not apply. In that reading, the model expresses an instruction to the people who will build the real world agent. To do it correctly, they must ensure that the policy function inside the compute core they build will remain unmodified. In section 11 of the paper, I discuss in more detail how this design goal might be achieved in the case of an AGI agent.

Graphical Construction of Counterfactuals

We now show how mathematical counterfactuals can be defined using graphical models. The process is as follows. We start by drawing a first diagram $f$, and declare that this $f$ is the world model of a factual world. This factual world may be the real world, but also an imaginary world, or the world inside a simulator. Next, we draw a second diagram $c$ by taking $f$ and making some modifications. We then posit that this $c$ defines a counterfactual world. The counterfactual random variables defined by $c$ then represent observations we can make in this counterfactual world.

The diagrams below show an example of the procedure, where we construct a counterfactual game world in which the red die has the number 6 on all sides.

We name diagrams by putting a label in the upper left hand corner. The two labels (f) and (c) introduce the names $f$ and $c$. We will use the name in the label for both the diagram, the implied world model, and the implied world. So the rightmost diagram above constructs the counterfactual game world $c$.

To keep the random variables defined by the above two diagrams apart, we use the notation convention that a diagram named $c$ defines random variables that all have the subscript $c$. Diagram $c$ above defines the random variables $X_c$, $Y_c$, and $S_c$. This convention allows us to write expressions like $P(S_c>S_f)=5/6$ without ambiguity.

Graphical Model of a World with an Agent

An AI agent is an autonomous system which is programmed to use its sensors and actuators to achieve specific goals.

Diagram $d$ below models a basic MDP-style agent and its environment. The agent takes actions $A_t$ chosen by the policy $\pi$, with actions affecting the subsequent states $S_{t+1}$ of the agent's environment. The environment state is $s_0$ initially, and state transitions are driven by the probability density function $S$.

We interpret the annotations above the nodes in the diagram as model input parameters. The model $d$ has the three input parameters $\pi$, $s_0$, and $S$. By writing exactly the same parameter above a whole time series of nodes, we are in fact adding significant constraints to the behavior of both the agent and the agent environment in the model. These constraints apply even if we specify nothing further about $\pi$ and $S$.

We use the convention that the physical realizations of the agent's sensors and actuators are modeled inside the environment states $S_t$. This means that we can interpret the arrows to the $A_t$ nodes as sensor signals which flow into the agent's compute core, and the arrows emerging from the $A_t$ nodes as actuator command signals which flow out.

The above model obviously represents an agent interacting with an environment, but is silent about what the policy $\pi$ of the agent looks like. $\pi$ is a free model parameter: the diagram gives no further information about the internal structure of $\pi$.

Causal Influence Diagrams as a Decision Theory

A Causal Influence Diagram is an extended version of a graphical agent model, which contains more information about the agent policy. We can read the diagram as a specification of a decision theory, as an exact specification of how the agent policy decides which actions the agent should take.

The Causal Influence Diagram $a$ defines a specific agent, interacting with the same environment seen earlier in $d$, by using:

• diamond shaped utility nodes $R_t$ which define the value $U_a$, the expected overall utility of the agent's actions as computed using the reward function $R$ and time discount factor $\gamma ,$ and

• square decision nodes $A_t$ which define the agent policy ${\pi }^{\ast }$.

The full mathematical definitions of the semantics of the diagram above are in the paper. But briefly, we have that $U_a=E\left({\sum }_{t=0}^{\infty }{\gamma }^t{R}_{t,a}\right)$, and we define ${\pi }^{\ast }$ by first constructing a helper diagram:

• Draw a helper diagram $b$ by drawing a copy of diagram $a$, except that every decision node has been drawn as a round node, and every ${\pi }^{\ast }$ has been replaced by a fresh function name, say ${\pi }^{\prime }$.

• Then, ${\pi }^{\ast }$ is defined by ${\pi }^{\ast }={\text{argmax}}_{{\pi }^{\prime }}{U}_b$, where the ${\text{argmax}}_{{\pi }^{\prime }}$ operator always deterministically returns the same function if there are several candidates that maximize its argument.

The above diagram defines the agent in the world $a$ as an optimal-policy agent.

We can interpret an optimal policy agent as one that is capable of exactly computing ${\pi }^{\ast }={\text{argmax}}_{{\pi }^{\prime }}{U}_b$ in its compute core, by computing $U_b$ for all possible different world models $b$, where each $b$ has a different ${\pi }^{\prime }$. This computation will have to rely on the agent knowing the exact value of $S$.

The optimal policy ${\pi }^{\ast }$ defined above is the same as the optimal policy ${\pi }^{\ast }$ that is defined in an MDP model, a model with reward function $R$, starting state $s_0$, and with $S(s^{\prime },s,a)$ being the probability that the MDP world will enter state $s^{\prime }$ if the agent takes action $a$ in state $s$. A more detailed comparison with MDP based and Reinforcement Learning (RL) based agent models is in the paper.

The Causal Influence Diagrams which I formally define in the paper are roughly the same as those defined and promoted by Everitt et al in 2019, with the most up to date version of the definitions and supporting explanations being here.

One difference is that I also fully define the semantics of diagrams representing multi-action decision making processes, not just the single-decision case. Another difference is that I explicitly name the structural functions of the causal model by writing annotations like $s_0$, ${\pi }^{\ast }$, $S$, and $R$ above the diagram nodes. The brackets around $\left[S\right]$ in the diagram indicate that this structural function is a non-deterministic function.

The above world model $d$ does not include any form of machine learning: its optimal-policy agent can be said to perfectly know its full environment $S$ from the moment it is switched on. A machine learning agent, on the other hand, will have to use observations to learn an approximation of $S$.

Two-Diagram Models of Online Machine Learning Agents

We now model online machine learning agents, agents that continuously learn while they take actions. These agents are also often called reinforcement learners. The term reinforcement learning (RL) has become somewhat hyped however. As is common in a hype, the original technical meaning of the term has become diluted: nowadays almost any agent design may end up being called a reinforcement learner.

We model online machine learning agents by drawing two diagrams, one for a learning world and one for a planning world, and by writing down an agent definition. This two-diagram modeling approach departs from the usual influence diagram based approach, where only a single diagram is used to model an entire agent or decision making process. By using two diagrams instead of one, we can graphically represent details which remain hidden from view, which cannot be expressed graphically, when using only a single diagram.

Learning world

Diagram $l$ is an example learning world diagram. The diagram models how the agent interacts with its environment, and how the agent accumulates an observational record $O_t$ that will inform its learning system, thereby influencing the agent policy $\pi$.

We model the observational record as a list all past observations. With $++$ being the operator which adds an extra record to the end of a list, we define that

$O(o_{t-1},s_{t-1},a_{t-1},s_t)=o_{t-1}++(s_t,s_{t-1},a_{t-1}).$

The initial observational record $O_0$ may be the empty list, but it might also be a long list of observations from earlier agent training runs, in the same environment or in a simulator.

We intentionally model observation and learning in a very general way, so that we can handle both existing machine learning systems and hypothetical future machine learning systems that may produce AGI-level intelligence. To model the details of any particular machine learning system, we introduce the learning function $L$. This $L$ which takes an observational record $o$ to produce a learned prediction function $L=L\left(o\right)$, where this function $L$ is constructed to approximate the $S$ of the learning world.

We call a machine learning system $L$ a perfect learner if it succeeds in constructing an $L$ that fully equals the learning world $S$ after some time. So with a perfect learner, there is a $t_p$ where ${\forall }_{t\ge t_p}P\left(L\right(O_{t,l})=S)=1$. While perfect learning is trivially possible in some simple toy worlds, it is generally impossible in complex real world environments.

We therefore introduce the more relaxed concept of reasonable learning. We call a learning system reasonable if there is a $t_p$ where ${\forall }_{t\ge t_p}P\left(L\right(O_{t,l})\approx S)=1$. The $\approx$ operator is an application-dependent good enough approximation metric. When we have a real-life implementation of a machine learning system $L$, we may for example define $L\approx S$ as the criterion that $L$ achieves a certain minimum score on a benchmark test which compares $L$ to $S$.

Planning world

Using a learned prediction function $L$ and a reward function $R$, we can construct a planning world $p$ for the agent to be defined. Diagram $p$ shows a planning world that defines an optimal policy ${\pi }_p^{\ast }$.

We can interpret this planning world as representing a probabilistic projection of the future of the learning world, starting from the agent environment state $s$. At every learning world time step, a new planning world can be digitally constructed inside the learning world agent's compute core. Usually, when $L\approx S$, the planning world is an approximate projection only. It is an approximate projection of the learning world future that would happen if the learning world agent takes the actions defined by ${\pi }_p^{\ast }$.

Agent definitions and specifications

An agent definition specifies the policy $\pi$ to be used by an agent compute core in a learning world. As an example, the agent definition below defines an agent called the factual planning agent, FP for short.

FP: The factual planning agent has the learning world $l$, where $\pi (o,s)={\pi }_p^{\ast }\left(s\right)$, with ${\pi }_p^{\ast }$ defined by the planning world $p$, where $L=L\left(o\right)$.

When we talk about the safety properties of the FP agent, we refer to the outcomes which the defined agent policy $\pi$ will produce in the learning world.

When the values of $S$, $s_0$, $O$, $O_0$, $L$, and $R$ are fully known, the above FP agent definition turns the learning world model $l$ into a fully computable world model, which we can read as an executable specification of an agent simulator. This simulator will be able to use the learning world diagram as a canvas to display different runs where the FP agent interacts with its environment.

When we leave the values of $S$ and $s_0$ open, we can read the FP agent definition as a full agent specification, as a model which exactly defines the required input/output behavior of an agent compute core that is placed in an environment determined by $S$ and $s_0$. The arrows out of the learning world nodes $S_t$ represent the subsequent sensor signal inputs that the core will get, and the arrows out of the nodes $A_t$ represent the subsequent action signals that the core must output, in order to comply with the specification.

Exploration

Many online machine learning system designs rely on having the agent perform exploration actions. Random exploration supports learning by ensuring that the observational record will eventually represent the entire dynamics of the agent environment $S$. It can be captured in our modeling system as follows.

FPX: The factual planning agent with random exploration has the learning world $l$, where $$\pi (o,s)=\{\begin{array}{cc}\text{RandomAction()}& \text{if}\text{RandomNumber()}\le X\\ {\pi }_p^{\ast }\left(s\right)& \text{otherwise}\end{array}$$ with ${\pi }_p^{\ast }$ defined by the planning world $p$, where $L=L\left(o\right)$.

Most reinforcement learning type agents can be modeled by creating variants of this FPX agent definition, and using specific choices for model parameters like $L$. I discuss this topic in more detail in section 10 of the paper.

The possibility of learned self-knowledge

It is possible to imagine agent designs that have a second machine learning system $M$ which produces an output $M\left(o\right)=M$ where $M\approx \pi$. To see how this could be done, note that every observation $(s_i,s_{i-1},a_{i-1})\in o$ also reveals a sample of the behavior of the learning world $\pi$: $\pi (‘o\text{up to}i-1\text{'},s_{i-1})=a_{i-1}$. While $L$ contains learned knowledge about the agent's environment, we can interpret $M$ as containing a type of learned compute core self-knowledge.

In philosophical and natural language discussions about AGI agents, the question sometimes comes up whether a sufficiently intelligent machine learning system, that is capable of developing self-knowledge $M$, won't eventually get terribly confused and break down in dangerous or unpredictable ways.

One can imagine different possible outcomes when such a system tries to reason about philosophical problems like free will, or the role of observation in collapsing the quantum wave function. One cannot fault philosophers for seeking fresh insights on these long-open problems, by imagining how they apply to AI systems. But these open problems are not relevant to the design and safety analysis of factual and counterfactual planning agents.

In the agent definitions of the paper, I never use an $M$ in the construction of a planning world: the agent designs avoid making computations that project compute core self-knowledge.

The issue of handling and avoiding learned self-knowledge gets more complex when we consider machine learning systems which are based on partial observation. I discuss this more complex case in sections 10.2 and 11.1 of the paper.

A Counterfactual Planner with a Short Time Horizon

For the factual planning FP agent above, the planning world projects the future of the learning world as well as possible, given the limitations of the agent's learning system. To create an agent that is a counterfactual planner, we explicitly construct a counterfactual planning world that creates an inaccurate projection.

As a first example, we define the short time horizon agent STH that only plans N time steps ahead in its planning world, even though it will act for an infinite number of time steps in the learning world.

The STH agent has the same learning world $l$ as the earlier FP agent:

but it uses the counterfactual planning world $st$, which is limited to $N$ time steps:

The STH agent definition uses these two worlds:

STH: The short time horizon agent has the learning world $l$, where $\pi (o,s)={\pi }_s^{\ast }\left(s\right)$, with ${\pi }_s^{\ast }$ defined by the planning world $st$, where $L=L\left(o\right)$.

Compared to the FP agent which has an infinite planning horizon, the STH agent has a form of myopia that can be interesting as a safety feature:

• Myopia implies that the STH agent will never put into motion any long term plans, where it invests to create new capabilities that only pay off after more than $N$ time steps. This simplifies the problem of agent oversight, the problem of interpreting the agent's actions in order to foresee potential bad outcomes.

• Myopia also simplifies the problem of creating a reward function that is safe enough. It will have no immediate safety implications if the reward function encodes the wrong stance on the desirability of certain events that can only happen in the far future.

• In a more game-theoretical sense, myopia creates a weakness in the agent that can be exploited by its human opponents if it would ever come to an all-out fight.

The safety features we can get from myopia are somewhat underwhelming: the next posts in this sequence will consider much more interesting safety features.

Whereas toy non-AGI versions of the FP and FPX agents can be trivially implemented with a Q-learner, implementing the a toy STH agent with a Q-learner is more tricky: we would have to make some modifications deep inside the Q-learning system, and switch to a data structure that is more complex than a simple Q-table. The trivial way to implement a toy STH agent is to use a toy version of a model-based reinforcement learner. I cover the topics of theoretical and practical implementation difficulty in more detail in the paper.