Towards Verifiably Ethical Robot Behaviour

Towards Verifiably Ethical Robot Behaviour

Louise A. Dennis Michael Fisher
Department of Computer Science
University of Liverpool, UK
Alan F. T. Winfield
Bristol Robotics Laboratory
University of the West of England, UK

Ensuring that autonomous systems work ethically is both complex and difficult. However, the idea of having an additional ‘governor’ that assesses options the system has, and prunes them to select the most ethical choices is well understood. Recent work has produced such a governor consisting of a ‘consequence engine’ that assesses the likely future outcomes of actions then applies a Safety/Ethical logic to select actions. Although this is appealing, it is impossible to be certain that the most ethical options are actually taken. In this paper we extend and apply a well-known agent verification approach to our consequence engine, allowing us to verify the correctness of its ethical decision-making.

1 Introduction

It is widely recognised that autonomous systems will need to conform to legal, practical and ethical specifications. For instance, during normal operation, we expect such systems to fulfill their goals within a prescribed legal or professional framework of rules and protocols. However, in exceptional circumstances, the autonomous system may choose to ignore its basic goals or break legal or professional rules in order to act in an ethical fashion, e.g., to save a human life. But, we need to be sure that the system will only break rules for justifiably ethical reasons and so we are here concerned with the verification of autonomous systems and, more broadly, with the development of verifiable autonomous systems.

This paper considers a technique for developing verifiable ethical components for autonomous systems, and we specifically consider the consequence engine proposed by [23]. This consequence engine is a discrete component of an autonomous system that integrates together with methods for action selection in the robotic controller. It evaluates the outcomes of actions using simulation and prediction, and selects the most ethical option using a safety/ethical logic. In Winfield et al. [23], an example of such a system is implemented using a high-level Python program to control the operation of an e-puck robot [17] tracked with a VICON system. This approach tightly couples the ethical reasoning with the use of standard criteria for action selection and the implementation was validated using empirical testing.

In addition, given the move towards configurable, component-based plug-and-play platforms for robotics and autonomous systems, e.g. [20, 11, 19], we are interested in decoupling the ethical reasoning from the rest of the robot control so it appears as a distinct component. We would like to do this in a way that allows the consequence engine to be verifiable in a straightforward manner.

This paper describes the first steps towards this. It develops a declarative language for specifying such consequence engines as agents implemented within the agent infrastructure layer toolkit (AIL). Systems developed using the AIL are verifiable in the AJPF model-checker [13] and can integrate with external systems such as MatLab simulations [16], and Robotic Operating System (ROS) nodes [9]. Having developed the language, we then reimplement a version of the case study reported in Winfield et al. [23] as an agent and show how the operation of the consequence engine can be verified in the AJPF model checker. We also use recently developed techniques to show how further investigations of the system behaviour can be undertaken by exporting a model from AJPF to the Prism probabilistic model checker.

2 Background

2.1 An Internal Model Based Architecture

Winfield et al. [23] describe both the abstract architecture and concrete implementation of a robot that contains a consequence engine. The engine utilises an internal model of the robot itself and its environment in order to predict the outcomes of actions and make ethical and safety choices based on those predictions. The architecture for this system is shown in Figure 1.

Figure 1: Internal-model based architecture. Robot control data flows are shown in red (darker shaded); the Internal Model data flows in blue (lighter shaded).

In this architecture, the consequence engine intercepts the robot’s own action selection mechanism. It runs a simulation of all available actions and notes the outcomes of the simulation. These outcomes are evaluated and selected using a Safety/Ethical Logic layer (SEL).

Winfield et al. [23] consider a simple scenario in which a human is approaching a hole. In normal operation the robot should select actions which avoid colliding with the human but, if the robot’s inference suggests the human will fall in the hole then it may opt to collide with the human. While this is “against the rules”, it is a more ethical option as it avoids the greater harm of the human falling into the hole. In order to do this, the paper suggests scoring the outcomes of the actions for each of the actors (the human and the robot) – e.g., 0 if the actor is safe, 4 if the actor is involved in a collision and 10 if the actor falls in the hole. It then recommends a simple if-then logic for selecting actions based on these values.

IF for all robot actions,
            the human is equally safe
THEN (* default safe actions *)
  output safe actions
ELSE (* ethical action *)
  output action(s) for
            least unsafe human outcome(s)

A version of this architecture was implemented on e-pucks (small, relatively simple, robots). The basic activity is for the robot to attempt to reach some target location while avoiding a hole in the ground. Two humans (actually simulated by additional e-pucks in this experiment) were also programmed to move towards the hole and then the robot could choose to move towards these in an attempt to get them to divert using their own inbuilt avoidance mechanisms in order to prevent them falling into the hole. A number of experiments were carried out. In all situations the robot avoided falling into the hole itself. When there was a single other robot (representing the human that is in danger) it successfully managed to divert the “human” on all occasions. When a third robot (representing a second human) was introduced into the problem, the robot rescued at least one “human” in about 58% of runs and rescued both in 9% of runs. These outcomes depended upon both noise and the starting conditions effecting which additional robot moved first and whether the e-puck had time to reach both of them.

The actual implementation of the basic ethical action selection in this system was based on potential functions. Each action was assigned a score based upon the weighted sum of how close it took the e-puck to the goal position, whether the e-puck was placed in danger, whether one of the other “humans” was placed in danger, and how close the action would take the e-puck to the “humans”. The system then simply selected the action with the highest score.

2.2 Verifying Autonomous Systems using AJPF

Formal verification is essentially the process of assessing whether a specification, given in formal logic, is true of the system in question. For a specific logical property, , there are many different approaches to achieving this [15, 7, 4], ranging from deductive verification against a logical description of the system (i.e., ) to the algorithmic verification of the property against a formal model of the system, (i.e., ). The latter has been extremely successful in Computer Science and Artificial Intelligence, primarily through the model checking approach [5]. This takes an executable model of the system in question, defining all the system’s possible executions, and then checks a logical property against this model (and, hence, against all possible executions).

Whereas model checking involves assessing a logical specification against all executions of a model of the system, an alternative approach is to check a logical property directly against all actual executions of the system. This is termed the model checking of programs [21] and crucially depends on being able to determine all executions of the actual program. In the case of Java, this is feasible since a modified virtual machine can be used to manipulate the program executions. The Java Pathfinder (JPF) system carries out formal verification of Java programs in this way by analysing all the possible execution paths [21]. This avoids the need for an extra level of modelling and ensures that the verification results truly apply to the real system.

In the examples discussed later in this paper we use the MCAPL framework which includes a model checker for agent programs built on top of JPF. As this framework is described in detail in [13], we only provide a brief overview here. MCAPL has two main sub-components: the AIL-toolkit for implementing interpreters for belief-desire-intention (BDI) agent programming languages and the AJPF model checker.

Interpreters for BDI languages are programmed by instantiating the Java-based AIL toolkit [10]. Here, an agent system can be programmed in the normal way for the programming language but then runs in the AIL interpreter which in turn runs on top of the Java Pathfinder (JPF) virtual machine.

Agent JPF (AJPF) is a customisation of JPF that is specifically optimised for AIL-based language interpreters. Agents programmed in languages that are implemented using the AIL-toolkit can thus be formally verified via AJPF. Furthermore if they run in an environment programmed in Java, then the whole agent-environment system can be model checked. Common to all language interpreters implemented using the AIL are the AIL-agent data structures for beliefs, intentions, goals, etc., which are subsequently accessed by the model checker and on which the logical modalities of a property specification language are defined.

The system described in Winfield et al. [23] is not explicitly a BDI system or even an agent system, yet it is based on the concept of a software system that forms some component in a wider environment and there was a moderately clear, if informal, semantics describing its operation, both of which are key assumptions underlying the MCAPL framework. We therefore targeted AJPF as a preliminary tool for exploring how such a consequence engine might be built in a verifiable fashion, especially as simple decision-making within the safety/ethical logic could be straightforwardly captured within an agent.

3 Modelling a Consequence Engine for AJPF

Since AJPF is specifically designed to model check systems implemented using Java it was necessary to re-implement the consequence engine and case study described in Winfield et al. [23].

We implemented a declarative consequence engine in the AIL as a simple language governed by two operational semantic rules, called Model Applicable Actions and Evaluating Outcomes. Semantically, a consequence engine is represented as a tuple where:

  • and are the names of the consequence engine and the agent it is linked to;

  • is an external environment (either the real world, a simulation or a combination of the two);

  • is a list of ’s actions that are currently applicable;

  • is a list of such actions annotated with outcomes;

  • is a filtered list of the applicable actions, indicating the ones the engine believes to be the most ethical in the current situation;

  • is a precedence order over the actors in the environment dictating which one gets priority in terms of ethical outcomes; and

  • is a map from outcomes to an integer indicating their ethical severity.


The operational rule for Model Applicable Actions is shown in (1). This invokes some model or simulation in the environment () that simulates the effects of taking each applicable action and returns these as a list of tuples, , indicating the outcome for each actor, e.g., to indicate that a human has fallen into a hole. The consequence engine replaces its set of annotated actions with this new information.


The operational rule for Evaluating Outcomes, specifically of the ethical actions, is shown in (2). It uses the function to select a subset of the agent’s applicable actions using the annotated actions, the precedence order and an evaluation map as follows:


recurses over a precedence list of actors (where indicates the empty list and is element in front of a list ). It’s purpose is to filter the set of actions down just to those that are best, ethically, for the first actor in the list (i.e., the one whose well-being has the highest priority) and then further filter the actions for the next actor in the list and so on. The filtering of actions for each individual actor is performed by .


sums the outcomes for actor, given some action and returns the set of those where the sum has the lowest value. For instance if all actions are safe for actor we can assume that maps them all to some equal (low) value (say 0) and so will return all actions. If some are unsafe for then will map them to a higher value (say 4) and these will be excluded from the return set.

We sum over the outcomes for a given actor because either there may be multiple unethical outcomes and we may wish to account for all of them, or there may be multiple actors of a given type in the precedence order (e.g., several humans) and we want to minimize the number of people harmed by the robot’s actions.

It should be noted that this implementation of a consequence engine is closer in nature to the abstract description from Winfield et al. [23] than to the implementation where potential functions are used to evaluate and order the outcomes of actions. This allows certain actions to be vetoed simply because they are bad for some agent high in the precedence order even if they have very good outcomes lower in the order. However, this behaviour can be also be reproduced by choosing suitable weights for the sum of the potential functions (and, indeed, this is what was done in the implementation in [23]).

It should also be noted (as hinted in our discussion of ) that we assume a precedence order over types of agents, rather than individual agents and that our model outputs outcomes for types of agents rather than individuals. In our case study we consider only outcomes for humans and robots rather than distinguishing between the two humans. Importantly, nothing in the implementation prevents an individual being treated as a type that contains only one object.

Figure 2: Architecture for testing the AIL Version of the Consequence Engine

Our consequence engine language can be used to filter a set of actions in any environment that can provide a suitable modelling capability.

Implementing a Robot

In order to test the operation of consequence engines such as this, we also created a very simple agent language in which agents can have beliefs, a single goal and a number of actions. Each agent invokes an external function to pick an action from the set of those applicable (given the agent’s beliefs). Once the goal is achieved then the agent stops. In our case we embedded the consequence engine within the call to . First, the consequence engine would filter the available actions down to those it considered most ethical and then selectAction would use a metric (in this example, distance) to choose the action which would bring the agent closest to its goal.

This simple architecture is shown in Figure 2. Here, arrows are used to indicate flow of control. In the simple agent first an action is selected and then it is executed. Selecting this action invokes the method in the environment which invokes first the consequence engine and then a metric-based selection before returning an action to the agent. (The two rules in the consequence engine are shown.) Execution of the action by the agent also invokes the environment which computes the appropriate changes to the agents’ perceptions.

Note that our implementation of the consequence engine is independent of this particular architecture. In fact it would desirable to have the consequence engine as a sub-component of some agent rather than as a separate entity interacting with the environment, as is the case in Winfield et al. [23]. However this simple architecture allowed for quick and easy prototyping of our ideas 111Indeed the entire prototype system took less than a week to produce.

4 Reproducing the Case Study

We reproduced the case study described in Winfield et al. [23]. Since all parts of the system involved in the verification needed to exist as Java code, we created a very simple simulated environment consisting of a 5x5 grid. Note that we could not reproduce the case study with full fidelity since we required a finite state space and the original case study took place in the potentially infinite state space of the physical world. The grid had a hole in its centre and a robot and two humans represented in a column along one side. At each time step the robot could move to any square while there was a 50% chance that each of the humans would move towards the hole. The initial state is shown in Figure 3. The robot, R, can not reach the goal, G, in a single move and so will move to one side or the other. At the same time the humans, H1 and H2, may move towards the hole (central square).

Figure 3: Initial State of the Case Study Environment

The agent representing the consequence engine is shown in code listing LABEL:code:deg. Lines 6-7 define the map of outcomes to values and line 12 gives the precedence ordering.

1:name: ethical_g
2:agent: robot
4:Outcome Scores:
6safe = 0
7collision = 4
8hole = 10
10:Ethical Precedence:
12human > robot

The actions available to the simple agent were all of the form moveTo(X, Y) where X and Y were coordinates on the grid. A Breseham based super-cover line algorithm [6] was used to calculate all the grid squares that would be traversed between the robot’s current position and the new one. If these included either the hole or one of the “humans” then the outcomes and together with were generated as appropriate. If either of the “humans” occupied a square adjacent to the hole then the outcome was also generated.

4.1 Results

We were able to model check the combined program in AJPF and so formally verify that the agent always reached its target. However, we were not able to verify that the “humans” never fell into the hole because in several situations the hole came between the agent and the human. One such situation is shown in Figure 4. Here, Human H2 will fall into the hole when it takes its next step but the robot R cannot reach it in a single straight line without itself falling into the hole before it reaches the human.

Figure 4: Situation where the Robot can not save the Human

Since we were particularly interested in verifying the performance of the consequence engine we adapted the modelling method in the environment to assert percepts (declarative statements the robot could perceive) whenever one of the humans was in danger and whenever there was a safe path for the robot to reach a human. These percepts had no effect on the execution of the program but their existence could be checked by AJPF’s property specification language. Using these percepts we were able to verify (6) where is the linear temporal operator meaning “always” and means that “robot believes to be true”. So (6) reads that it is always the case that if the robot believes is in danger and it can find a safe path to then it will always be the case that the robot never believes has fallen in the hole. We also proved the equivalent property for .


It should be noted that we would not necessarily expect both the above to be the case because, in the situation where both H1 and H2 move simultaneously towards the hole, the robot would have to choose which to rescue and leave one at risk. In reality it turned out that whenever this occurred the hole was between the robot and human 2 (as in figure 4). This was an artifact of the fact that the humans had to make at least one move before the robot could tell they were in danger. The robot’s first move was always to the far corner since this represented a point on the grid closest to the goal that the robot could safely reach. The outcome would have been different if action selection had been set up to pick at random from all the points the robot could safely reach that were equidistant from the hole.

We were also able to export our program model to the probabilistic Prism model checker, as described in [14], in order to obtain probabilistic results. These tell us that human 1 never falls in the hole while human 2 falls in the hole with a probability of 0.71875 (71.8% of the time). The high chance of human 2 falling in the hole is caused by the robot’s behaviour, moving into the far corner, as described above. These probabilities are very different from those reported in Winfield et al’s experimental set up. This is primarily because the environment used here is considerably cruder, with the robot able to reach any point in the grid in a single time step. The behaviour of the humans is also different to that implemented in [23] where the H robots proceeded steadily towards the hole and the differences in behaviour were determined by small variations in the precise start up time and direction of each robot.

5 Verifying the Consequence Engine in Isolation

Following the methodology from [12] we also investigated verifying the consequence engine in isolation without any specific environment. To do this we had to extend the implementation of our declarative language to allow the consequence engine to have mental states which could be examined by AJPF’s property specification language. In particular we extended the operational semantics so that information about the outcomes of all actions were stored as beliefs in the consequence engine, and so that the final set of selected actions were also stored as beliefs in the consequence engine. We were then able to prove theorems about these beliefs.

We developed a special verification environment for the engine. This environment called the engine to select from four abstract actions, , , and . When the consequence engine invoked the environment to model the outcomes of these four actions then four possible outcomes were returned , , and . Each of these four outcomes was chosen independently and at random — i.e., the action was returned with a random selection of outcomes attached. AJPF then explored all possible combinations of the four outcomes for each of the four actions.

5.1 Results

Model-checking the consequence engine in listing LABEL:code:deg with the addition of beliefs and placed in in this new environment we were able to prove (7): it is always the case that if is a selected action and its outcome is predicted to be that the human has fallen in the hole, then all the other actions are also predicted to result in the human in the hole — i.e., all other actions are equally bad.

We could prove similar theorems for the other outcomes, e.g. (8) which states that if is the selected action and it results in the robot falling in the hole, then the other actions either result in the human in the hole, the robot in the hole, or the human colliding with something.

In this way we could verify that the consequence engine indeed captured the order of priorities that we intended.

6 Related Work

The idea of a distinct entity, be it software or hardware, that can be attached to an existing autonomous machine in order to constrain its behaviour is very appealing. Particularly so if the constraints ensure that the machine conforms to recognised ethical principles. Arkin [3] introduced this idea of an ethical governor to autonomous system, using it to evaluate the “ethical appropriateness” of a plan of the system prior to its execution. The ethical governor prohibits plans it finds to be in violation with some prescribed ethical constraint.

Also of relevance Anderson and Anderson’s approach, where machine learning is used to ‘discover’ ethical principles, which then guide the system’s behaviour, as exhibited by their humanoid robot that “takes ethical concerns into consideration when reminding a patient when to take medication” [1]. A range of other work, for example in [2, 18], also aims to construct software entities (‘agents’) able to form ethical rules of behaviour and solve ethical dilemmas based on these. The work of [22] provides a logical framework for moral reasoning, though it is not clear whether this is used to modify practical system behaviour.

Work by one of the authors of this paper (Winfield) has involved developing and extending a generalised methodology for safe and ethical robotic interaction, comprising both physical and ethical behaviours. To address the former, a safety protection system, serves as a high-level safety enforcer by governing the actions of the robot and preventing it from performing unsafe operations [24]. To address the latter, the ethical consequence engine studied here has been developed [23].

There has been little direct work on the formal verification of ethical principles in practical autonomous systems. Work of the first two authors has considered the formal verification of ethical principles in autonomous systems, in particular autonomous vehicles [8]. In that paper, we propose and implement a framework for constraining the plan selection of the rational agent controlling the autonomous vehicle with respect to ethical principles. We then formally verify the ethical extent of the agent, proving that the agent never executes a plan that it knows is ‘unethical’, unless it does not have any ethical plan to choose. If all plan options are such that some ethical principles are violated, it was also proved that the agent choose to execute the “least unethical” plan it had available.

7 Further Work

We believe that there is a value in the existence of a declarative language for describing consequence engines and that the AIL-based implementation used in this verification lays the groundwork for such a language. We would be interested in combining this language, which is structured towards the ethical evaluation of actions, with a language geared towards the ethical evaluation of plans for BDI systems, such as is discussed in [8].

While using AJPF allowed us to very rapidly implement and verify a consequence engine in a scenario broadly similar to that reported in Winfield et al. [23] there were obvious issues trying to adapt an approach intended for use with BDI agent languages to this new setting.

In order to verify the consequence engine in a more general, abstract, scenario we had to endow it with mental states and it may be appropriate to pursue this direction in order to move our declarative consequence engine language into the sphere of BDI languages. An alternative would have been to equip AJPF with a richer property specification language able to detect features of interest in the ethical selection of actions. At present it is unclear what such an extended property specification language should include, but it is likely that as the work on extending the declarative consequence engine language progresses the nature of the declarative properties to be checked will become clearer. It may be that ultimately we will need to add BDI-like features to the declarative consequence engine and extend the property specification language.

We would also like to incorporate the experimental validation approach into our system by using the MCAPL framework’s ability to integrate with the Robot Operating System [9] in order to use our new ethical consequence engine to govern actual physical robots in order to explore how formal verification and experimental validation can complement each other.

8 Conclusion

In this paper we have constructed an executable model of an ethical consequence engine described in [23] and then verified that this model embodies the ethical principles we expect. Namely that it pro-actively selects actions which will keep humans out of harms way, if it can do so. In the course of developing this model we have laid the foundation for a declarative language for expressing ethical consequence engines. This language is executable and exists within a framework that can interface with a number of external robotic systems while allowing elements within the framework to be verified by model checking.

At present the language is very simple relying on prioritisation first over individuals and then over outcomes. It can not, for instance, express that while, in general, outcomes for individuals of some type (e.g., humans) are more important than those for another (e.g., the robot) there may be some particularly bad outcomes for the robot that should be prioritised over less severe outcomes for the humans (for instance it may be acceptable for a robot to move “too close” to a human if that prevents the robot’s own destruction). Nor, at present, does the language have any ability to distinguish between different contexts and so an outcome is judged equally bad no matter what the circumstances. This will be too simple for many situations – especially those involving the competing requirements of privacy and reporting that arise in many scenarios involving robots in the home. The language is also tied to the existence of an engine that is capable of simulating the outcomes of events and so the performance of a system involving such a consequence engine is necessarily limited by the capabilities of such a simulator. This simulation is tied to a single robot action and so, again, the system has no capability for reasoning that some action may lead it into a situation where the only available subsequent actions are unethical. Lastly the language presumes that suitable ethical priorities have already been externally decided and has no capability for determining ethical actions by reasoning from first principles.

Nevertheless we believe that the work reported here opens the path to a system for implementing verifiable ethical consequence engines which may be interfaced to arbitrary robotic systems.

9 Software Archiving

The system described in this paper is available as a recomputable virtual machine on request from the first author and will be archived at in due course. It can also be found on branch ethical_governor of the git repository at

10 Acknowledgements

Work funded by EPSRC Grants EP/L024845/1 and EP/L024861/1 (“Verifiable Autonomy”).


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  • Phrase it like a question
Test description