TextToHBM: A Generalised Approach to
Learning Models of Human Behaviour for
Activity Recognition from Textual Instructions
University of Rostock
There are various knowledge-based activity recognition approaches that rely
on manual deﬁnition of rules to describe user behaviour. These rules are later
used to generate computational models of human behaviour that are able to reason
about the user behaviour based on sensor observations. One problem with these
approaches is that the manual rule deﬁnition is time consuming and error prone
process. To address this problem, in this paper we outline an approach that learns
the model structure from textual sources and later optimises it based on observa-
tions. The approach includes extracting the model elements and generating rules
from textual instructions. It then learns the optimal model structure based on obser-
vations in the form of manually created plans and sensor data. The learned model
can then be used to recognise the behaviour of users during their daily activities.
We illustrate the approach with an example from the cooking domain.
Some activity recognition (AR) approaches utilise human behaviour models (HBM) in
the form of rules. These rules are used to generate probabilistic models with which the
system can infer the user actions and goals [9, 17, 12]. Such types of models are also
known as computational state space models (CSSM) . They treat activity recog-
nition as a plan recognition problem, where given an initial state, a set of possible
actions, and a set of observations, the executed actions and the user goals have to be
recognised . These approaches rely on prior knowledge to obtain the context infor-
mation needed for building the user actions and the problem domain. The prior knowl-
edge is provided by a domain expert or by the model designer. This knowledge is then
used to manually build a CSSM. The manual modelling is however time consuming
and error prone .
To address this problem, different works propose the learning of models from sen-
sor data . One problem these approaches face is that sensor data is expensive .
Furthermore, sensors are sometimes unable to capture ﬁne-grained activities , thus,
they might potentially not be learned.
To reduce the need of domain experts and / or sensor data, one can substitute them
with textual data . More precisely, one can utilise the knowledge encoded in textual
instructions to learn the model structure. Textual instructions specify tasks for achiev-
ing a given goal without explicitly stating all the required steps . On the one hand,
this makes them a challenging source for learning a model . On the other hand, they
are usually written in imperative form, have a simple sentence structure, and are highly
organised. Compared to rich texts, this makes them a better source for identifying the
sequence of actions needed for reaching the goal .
According to , to learn a model of human behaviour from textual instructions,
the system has to: 1. extract the actions’ semantics from the text, 2. learn the model
semantics through language grounding, 3. and, ﬁnally, to translate it into compu-
tational model of human behaviour for planning problems. To address the problem
of learning models of human behaviour for AR, we extend the steps proposed by .
We add the need of 4. learning the domain ontology that is used to abstract and / or
specialise the model. We also replace step 3. (models for planning problems) with com-
putational models for activity recognition as the targeted model format, as they are
able to reason about the human behaviour based on noisy or ambiguous observations
The contribution of this paper is twofold: (1) we present an approach for learn-
ing HBM from textual instructions. In difference to existing approaches for language
grounding, our approach learns a complex domain ontology that is later used to gener-
alise or specialise the model; (2) it is the ﬁrst attempt at learning CSSMs for activity
recognition from textual instructions. In the following we outline our approach for
learning HBM for AR and illustrate it with an example from the kitchen domain. This
work is based on the extended abstract in .
There are various approaches to learning models of human behaviour from textual in-
structions: through grammatical patterns that are used to map the sentence to a machine
understandable model of the sentence [26, 2]; through machine learning techniques
[18, 5]; or through reinforcement learning approaches that learn language by interact-
ing with an external environment [2, 3].
Models learned through model grounding have been used for plan generation [13,
2], for learning the optimal sequence of instruction execution , for learning nav-
igational directions , and for interpreting human instructions for robots to follow
them [10, 19]. To our knowledge, any attempts to apply language grounding to learn-
ing models for AR rely on identifying objects from textual data and do not build a
computational model of human behaviour . This, however, suggests that models
learned from text could be used for AR tasks.
Existing approaches that learn human behaviour from text make simplifying as-
sumptions about the problem, making them unsuitable for more general AR problems.
More precisely, the preconditions and effects are learned through explicit causal rela-
tions, that are grammatically expressed in the text [13, 18]. They however, either rely
on initial manual deﬁnition to learn these relations , or on grammatical patterns and
rich texts with complex sentence structure . They do not address the problem of
discovering causal relations between sentences, but assume that all causal relations are
expressed within the sentence . They also do not identify implicit relations.
However, to ﬁnd causal relations in instructions without a training phase, one has to
rely on alternative methods, such as time series analysis . Moreover, the initial state
is manually deﬁned and there are only a few works that identify possible goals based
on the textual instructions [26, 1]. This limits the approaches to a predeﬁned problem
and does not allow the reasoning about different situations and goals. This is, however,
an important requirement for any assistive system that relies on activity recognition.
Furthermore, they rely on manually deﬁned ontology, or do not use one. However,
one needs an ontology to deal with model generalisation problems and as a means for
expressing the semantic relations between model elements.
Moreover, there have been previously no attempts at learning CSSMs from textual
instructions. Existing CSSM approaches rely on manual rules’ deﬁnition to build the
preconditions and effects of the models. For example,  use the cognitive architecture
ACT-R while other approaches rely on a PDDL1-like notations to describe the possible
actions [17, 12]. In that sense, our work is the ﬁrst attempt at learning CSSMs from
In this work we represent the rules in a PDDL-like notation in the form described
Identifying text elements of interest
To extract the text elements that describe the user behaviour, the user actions and their
relations to other entities in the environment have to be identiﬁed. This is achieved
through assigning each word in a text the corresponding part of speech (POS) tag. Fur-
thermore, the dependencies between text elements are identiﬁed through dependencies
parser. To identify the human actions, the verbs from the POS-tagged text are extracted.
We are interested in present tense verbs, as textual instructions are usually written in
present tense, imperative form. We also extract any nouns that are direct objects to the
actions. These will be the objects in the environment with which the human can inter-
act. Furthermore, we extract any nouns that are in conjunction to the identiﬁed objects.
These will have dependencies to the same actions, to which the objects with which
they are in conjunction are dependent. Figure 1 gives an example of a sentence and the
identiﬁed elements based on the POS-tags and dependencies.
Moreover, any preposition relations such as in,on,at, etc. between the objects and
other elements in the text are identiﬁed. These provide spacial or directional informa-
tion about the action of interest. For example, in the sentence “Put the apple on the
table.” our action is put, while the object on which the action is executed is apple. The
action is executed in the location table identiﬁed through the on preposition. Finally,
1Planning Domain Deﬁnition Language
Take the clean knife from the counter.
VB DT NN IN DT NN
Action Object Location (from)
Figure 1: Elements of a sentence necessary for the model learning.
we extract “states” from the text. The state of an object is the adjectival modiﬁer or
the nominal subject of an object. As in textual instructions the object is often omitted
(e.g. “Simmer (the sauce) until thickened.”), we also investigate the relation between
an action and past tense verbs or adjectives that do not belong to an adjectival modi-
ﬁer or to nominal subject, but that might still describe this relation. The states give us
information about the state of the environment before and after an action is executed.
Extracting causal relations from textual instructions
To identify causal relations between the actions, and between states and actions, we
use an approach proposed in . It transforms every word of interest in the text into
a time series and then applies time series analysis to identify any causal relations be-
tween the series. More precisely, each sentence is treated as a time stamp in the time
series. Then, for each word of interest, the number of occurrences it appears in the
sentence is counted and stored as element of the time series with the same index as the
sentence index. We generate time series for all actions and for all states that change
an object. To discover causal relations based on the time series, we apply the Granger
causality test. It is a statistical test for determining whether one time series is useful for
forecasting another. More precisely, Granger testing performs statistical signiﬁcance
test for one time series, “causing” the other time series with different time lags using
auto-regression . The causality relationship is based on two principles. The ﬁrst is
that the cause happens prior to the effect, while the second states that the cause has a
unique information about the future values of its effect. Given two sets of time series
xtand yt, we can test whether xtGranger causes ytwith a maximum ptime lag. To do
that, we estimate the regression yt=ao+a1yt−1+...+apyt−p+b1xt−1+... +bpxt−p.
An F-test is then used to determine whether the lagged xterms are signiﬁcant.
Figure 2 shows the procedure of converting text elements into time series and using
them to discover causal relations.
Take the knife from the counter.
Cut the carrots.
Put the knife on the counter.
Take the pot from the counter.
Put the pot on the stove.
Put the carrots in the pot.
Turn on the stove.
Take the wooden spoon from the counter.
Put the wooden spoon in the pot.
Instruction: cook a carrot soup
X (Take) Y (Put)
yt = ao+a1yt−1+...+apyt−p
+b1xt−1+ ... + bp xt−p
The lagged x
signiﬁcant for the
prediction of y?
xt Granger causes
yt => "take"
Figure 2: The procedure for discovering causal relations.
Building the domain ontology
The domain ontology is divided into argument and action ontology. The argument on-
tology describes the objects, locations, and any elements in the environment that are
taken as arguments in the actions. The action ontology represents the actions with their
arguments and abstraction levels.
To learn the argument ontology, a semantic lexicon (e.g. WordNet ) is used to
build the initial ontology. As the initial ontology does not contain some types that unify
arguments applied to the same action, the ontology has to be extended. To do that, the
prepositions with which actions are connected to indirect objects are also extracted
(e.g. in,on, etc.). They are then added to the argument ontology as parents of the
arguments they connect. In that manner, the locational properties of the arguments
are described (e.g. water has the property to be in something). During the learning
of the action templates and their preconditions, additional parent types are added to
describe objects used in actions that have the same preconditions. Furthermore, types
that are not present in the initial ontology, but which objects are used only in a speciﬁc
action, are combined in a common parent type. Figure 3 (left) shows an example of
an argument ontology. To learn the action ontology, the process proposed in  is
adapted for learning from textual data. Based on the argument ontology, the actions are
abstracted by replacing the concrete arguments with their corresponding types from
an upper abstraction level. In that manner, the uppermost level will represent the most
abstract form of the action. For example, the sentence “Put the apple on the table.” will
yield the concrete action put apple table, and the abstract action put object location.
Figure 3 (right) shows an example of an action ontology. This representation is used as
a basis for the precondition-effect rules that describe the actions.
Argument ontology Action ontology
Figure 3: Argument ontology (left): objects identiﬁed through POS-tagging and depen-
dencies (blue); hierarchy identiﬁed through WordNet (black); types identiﬁed through
the relations of objects to prepositions (red); types identiﬁed based on similar precon-
ditions (green); types identiﬁed through action abstraction (yellow). Action ontology
(right): abstract representation of an action (uppermost layer); abstract representation
of action take; concrete instances of action take (bottom layer).
Generating precondition-effect rules
The next step in the process is the generation of precondition-effect rules that describe
the actions and the way they change the world. The basis for the rules is the action
ontology. Each abstract action from the ontology is taken and converted to an action
template that has the form shown in Figure 4. Basically, the action name is the ﬁrst
:parameters (?o - object ?to - location)
(not (executed-put ?o ?to)))
(executed-put ?o ?to))
Figure 4: Example of an action template put in the PDDL notation.
part of the abstract entity put object location, while the two parameters are the second
and the third part of the entity. Furthermore, the default predicate (executed-action) is
added to both the precondition and the effect, whereas in the precondition it is negated.
Now the causal relations extracted from the text are used to extend the actions.
The execution of each action that was identiﬁed to cause another action is added as a
precondition to the second action. For example, to execute the action put, the action
take has to take place. That means that the predicate executed-take ?o has to be added
to the precondition of the action put. Furthermore, any states that cause the action are
also added in the precondition. For example, imagine the example sentence is extended
in the following manner: “If the apple is ripe, put the apple on the table.” In that case
the state ripe causes the action put. For that reason the predicate (state-ripe) will also
be added to the precondition. This procedure is repeated for all available actions. The
result is a set of candidate rules that describe a given behaviour.
As it is possible that some of the rules contradict each other, a reﬁnement step is
added. This is done by converting the argument ontology to the corresponding PDDL
format to represent the type hierarchy. The initial and goal states are then generated
by assigning different combinations of truth values to the set of predicates. Different
combinations of initial-goal states pairs are generated from the sets of initial and goal
states. Later, an initial-goal state pair as well as the rules and the type hierarchy are fed
to a planner and any predicates that prevent the reaching of the goal are removed from
the preconditions. This results in a set of candidate models from which the optimal
model will be selected.
Learning the optimal model structure
As the model will be applied to activity recognition tasks, it is important to learn
a model structure that optimises the probability of selecting the correct action2. To
achieved that, two steps are followed (see Figure 5). First, the model is optimised based
1. (take knife counter)
2. (cut carrots)
3. (put knife counter)
4. (take pot counter)
p(a1:T |Mn)remove from
p(a1:T |Ml)optimal model
T: a threshold value
a candidate model Mn is applied to a set of plans
a model Ml that explains the plans is applied to sensor-
based AR problem
Figure 5: Learning the optimal model for a given situation based on observations.
on its ability to explain existing plans describing the user behaviour. This approach is
similar to the methods for model learning through observations [3, 7]. Here, the obser-
vations are provided in the form of manually produced plans. The plans are obtained by
asking different persons to provide a plan based on textual description of a given task.
Models that are not able to predict the plan, receive no reward. From the remaining set
of models, those which maximise the probability of executing the plan above a given
2In our case, that is the actual action executed by the user.
threshold are selected. The probability is calculated based on Formula 1.
p(a1:T|M) = Y
λkfk(a)) ,new action (2)
Mis the model used to explain the plan, atis the action executed at time t,kis a set
of features, fis an action selection heuristic, and λis its weight. The action selection
heuristics are goal distance, landmarks, cognitive heuristics, etc .
After selecting the set of most promising models, they are further optimised. This
is done by testing their ability to recognise activities and goals based on sensor ob-
servations. As a base for this step, the validation steps from the development process
proposed in  are used. Formula 1 is once again used to select the model that best
explains the observations.
TextToHBM: an Example
To illustrate the approach, we take as an example an experiment description of a per-
son who is cooking a carrots soup. A description of the experiment can be found in
 and the sensor dataset itself in . This could be considered as a simpliﬁed ex-
ample, as the textual instruction contains explicit description of each execution step.
Table 1 shows an excerpt of the instructions provided for executing the experiment.
First, all actions in the dataset are identiﬁed3. For the carrots soup example, 15 actions
1Ta ke t h e k n i f e fr om t h e co u n t e r .
2Cu t th e c a r r o t s .
3P ut t h e k n i f e on th e c o u n t e r .
4Ta ke t h e po t fr om t h e c o u n t e r .
5P ut t h e p ot on th e s t o v e .
6P ut t he c a r r o t s i n th e p ot .
7Tu r n on th e s t o v e .
8Ta ke t h e w ood en s p oo n fr om t h e co u n t e r .
9P ut t h e w oo de n s po o n i n t h e p o t .
10 Coo k f o r 10 mi n u t e s .
11 Tu r n o f f t h e s t o v e .
12 Ope n t h e c u p b o a r d .
13 Ta k e a p l a t e f r om t h e c u p b o a r d .
14 Ta k e a g l a s s f ro m t h e c u p b o a r d .
15 P ut t h e p l a t e a nd t h e g l a s s on th e c o u n t e r .
Table 1: Excerpt from instruction describing the cooking of a carrot soup.
were identiﬁed. Furthermore, all arguments are identiﬁed. For this example, 19 argu-
ments were identiﬁed one of which was incorrectly labeled as noun (the verb “wash”).
Five of the arguments serve as locations (e.g. “counter”, “stove” etc.) describing places
3This can be done with the help of parser that POS-tags the text. Later, all present tense verbs are ex-
tracted, as they usually describe an action that is executed.
where actions are executed. The rest are objects upon which the action is executed (e.g.
“water”, “plate”, etc). Moreover, 7 prepositions were discovered that describe location,
direction or means by which an action is achieved (e.g. “in”, “from”, “with”). No states
were discovered in this example. This is due to the oversimpliﬁed sentence structure
that follows the pattern “action direct-object(s) location(s)”.
The next step is to represent each action as a time series. More precisely, each
element in the time series is represented with a number. This number indicates the
number of occurrences of the given action in the current sentence. In that manner, each
of the words (or pairs of words) of interest is assigned a time series. This allows the
utilisation of time series analysis for the discovery of implicit causal relations in textual
instructions. The resulting time series can be downloaded from .
Figure 6 shows the causal relations between actions discovered for cooking a car-
Figure 6: Causal relations discovered for cooking a carrot soup. Black indicates rela-
tions discovered by a human annotator, green: discovered with the proposed approach.
After identifying the causal relations, the argument ontology is learned. This is done
by feeding the identiﬁed nouns to WordNet in order to build the initial ontology. Then,
based on the relations described through prepositions, similar causal relations, and ab-
straction in the action ontology, the argument ontology is reﬁned and new relations are
identiﬁed. Figure 7 shows the resulting ontology and the steps for building it. Similarly
to the argument ontology, the action ontology is based on the identiﬁed actions, the ob-
jects they are executed on and the indirect objects or locations where they are executed.
food instrumentation container-area
Figure 7: Left: Learning the argument ontology. Step 1 (blue): objects identiﬁed
through POS-tagging and dependencies; step 2 (black): hierarchy identiﬁed through
WordNet; step 3 (red): types identiﬁed through the relations of objects to prepositions;
step 4 (green): types identiﬁed based on similar preconditions; step 5 (yellow): types
identiﬁed through action abstraction. Right: Learning the action ontology: (uppermost
layer): abstract representation; (middle layer): abstract representation of action “take”;
(bottommost layer): concrete representation of action “take””.
Each abstraction level of the action ontology is based on the corresponding abstraction
level in the argument ontology.
In the next step, based on the action ontology and the identiﬁed causal relations, the
precondition-effect rules are built. In this example, the rules are built based on 17 pred-
icates. As there were no states discovered, the predicates indicate whether an action is
executed or not (e.g. “(executed-wash ?f - wash-obj)”). Based on these rules, 20 action
templates were constructed. The templates are more than the action classes because the
same action class has different preconditions or effects in different situations. Figure 8
:parameters (?c - area)
(not (executed-close ?c))
(not (executed-open ?c))
:observation (setActivity (activity-id close))
Figure 8: The action template close in the PDDL notation.
shows the generated precondition-effect rule for the action “close”. It can be seen that
apart from the typical PDDL action notation, there are two additional slots: “:duration”
and “:observation”. These are later used for performing activity recognition. There, the
actions have durations and are observed through sensor observations. These slots al-
low linking the behaviour model to the underlying action duration distribution and the
expected type of observations.
Figure 9: Partially extended state space graph of the model.
After deﬁning the action rules, the next step is to generate the initial and goal states.
The initial and goal states represent different combinations of truth values over all
ground predicates. In our example we have 204 ground predicates which means that
we have 204! possible combinations. To reduce this number, we utilise some prior
knowledge. We assume that none of the objects is taken, no doors or cupboards are
open, and no devices are turned on. This means that the predicates (executed-close),
(executed-put), (executed-turn-off) are set to true. We also assume that apart from these,
no other actions have been executed at the beginning of the problem. This leaves us with
only one initial state. Similarly, for the goal state we assume that the actions “cook”,
“drink”, “eat”, and “wash” (applied to the different objects) have to be executed and for
the rest we do not care. As in the experiment we conducted to collect the sensor data,
different persons chose to wash different objects, we generate different goal conditions.
There, the predicates indicating the execution of the actions “cook”, “drink”, and “eat”
are always set to true, and the truth value of the predicates describing the execution of
the “wash” actions vary. As we have 7 objects on which “wash” can be executed, this
means we have 5040 combinations of goal conditions.
Having deﬁned the initial state and the goals, we use a state of the art planner to
identify any problems in the models that prevent reaching the goal state. For example,
the causal relation “ﬁll causes take” was discovered, which means that in the precon-
dition of “take” the predicate (executed-ﬁll) has to be true. However, this prevents the
model from reaching the goal state as no objects can be taken unless the action “ﬁll”
is executed. For that reason we remove this condition from the precondition of “take”.
This procedure is repeated for all models until all problems preventing the models from
reaching a goal state are removed.
The result of this step is a set of causally correct models that contain all execution
sequences from the initial state to the possible goal states. If the model can be fully
extended it can be seen as a directed graph where the nodes are states and the transitions
are actions. The graph starts with the initial state where the probability of this state is
one in the case of only one state, otherwise there is a probability distribution over all
initial states. The transitions from one state to another also have probabilities, which are
deﬁned based on action selection strategy such as the distance to the goal, or how often
the action is executed etc. Figure 9 shows an example of such graph where the dots are
the states and the connections between them are the actions. The graph starts with one
initial state and then follows different paths to the goal states (red dots at the bottom of
the graph). In this case the graph was only partially explored with iteratively deepening
depth ﬁrst search due to the large state space. The red line shows the sequence of actions
the user actually executed.
As we now have 5040 candidate models, we use a set of plans describing the execu-
tion sequences in the experiment we conducted. In this example the plans are generated
from the annotation of the video logs recorded during the experiment. This step reduces
the model to one goal condition where apart from “cook”, “eat”, and “drink”, “wash
glass”, “wash carrot”, and “wash plate” have to be executed. For the remaining predi-
cates, we do not care about their truth value4.
The resulting model has very large branching factor5. This reduces the probability
of selecting the actual action being executed, especially in the case of noisy or ambigu-
ous observations. For that reason an optimisation step is applied. Using the model and
sensor observations describing the execution sequences of different users preparing a
carrot soup, the transition probabilities are then adjusted and the observed sequences
receive higher probability than those that are not observed. In that manner, more typical
behaviour is more likely to be observed, but in the same time less probable behaviour is
not completely removed, so that in case of new observations, the model can be further
In this work we proposed an approach that automatically extracts knowledge from
textual instructions and learns models of human behaviour that are later used for ac-
tivity recognition. One problem the approach faces is the discovery of causal relations.
As textual instructions such as recipes are usually written in informal manner, their
sentences often lack the direct objects. This in turn makes it difﬁcult to discover the
objects on which an action is executed and also reduces the ability of the approach to
discover causal relations. For that reason, we believe that the approach could beneﬁt
from anaphora resolution techniques in order to include the missing direct objects to
Another problem is the generation of initial and goal states. As it could be seen from
the example, even with simplifying assumptions, there is a very large number of pos-
sible combinations. In that sense, the approach could beneﬁt of automated techniques
that discover improbable initial and goal conditions in the text. This could potentially
reduce the number of candidate models thus reducing the computational effort required
to evaluate the applicability of the models to the problem at hand.
Finally, the approach could potentially beneﬁt from utilising multiple textual in-
structions to learn the candidate models. This will allow the generation of richer models
4This one condition generates a set of possible goal states that can be reached. In other words, our model
now contains one initial state and several goal states in which the goal condition is satisﬁed.
5This is the maximum number of actions executable from a given state, or in other words the maximum
number of connections that leave a dot in Figure 9.
that contain more contextual information and that are not tailored for only one speciﬁc
Current Results and Future Work
In a previous work we proposed an approach of extracting causal relations from textual
instructions through time series analysis . We applied the approach to 20 textual in-
structions (cooking recipes, manuals, and experiment instructions). The results showed
that the approach is able to identify causal relations in simple texts with short sentences.
We compared the approach to one based on grammatical patterns and discovered that
the latter was able to detect very low number of relations. We used the extracted re-
lations as a basis for building precondition-effect rules . In  we were able to
learn a causal model describing the activities from the carrots soup preparation dataset
and to compare it to a manually built model described in . The results showed that
our approach is able to extract precondition-effect rules and to explain the plans cor-
responding to the video logs from the dataset. They, however, showed that the action
probability of executing the action described in the plan is very low given the model.
It also has to be mentioned, that the initial and goal state of the model were manually
In the future we will concentrate on optimising the textual instructions by applying
anaphora resolution techniques. We will also investigate techniques for reducing the
set of possible initial and goal states before the optimisation step. Furthermore, we will
investigate inverse reinforcement learning methods for optimising the model structure
from sparse observations. Finally, we plan to apply the approach to different domains
(such as physiotherapy and assistance of workers) in order to test its applicability to
various problems from the domain of daily activities.
If successful, the proposed approach will reduce the need of expert knowledge by
replacing it with the domain knowledge encoded in textual instructions. It, in turn, will
reduce the time and resources needed for developing computational models of human
behaviour for activity recognition. It will also be the ﬁrst attempt at actually applying
CSSMs learned from textual data to an activity recognition problem.
This work is funded by the German Research Foundation (DFG) within the context of
the project TextToHBM, grant number YO 226/1-1.
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