=Paper=
{{Paper
|id=Vol-3733/paper1
|storemode=property
|title=Geometric and Spatial Reasoning in BDI Agents: A Survey
|pdfUrl=https://ceur-ws.org/Vol-3733/paper1.pdf
|volume=Vol-3733
|authors=Angelo Ferrando,Andrea Gatti,Viviana Mascardi
|dblpUrl=https://dblp.org/rec/conf/cilc/00010M24
}}
==Geometric and Spatial Reasoning in BDI Agents: A Survey==
https://ceur-ws.org/Vol-3733/paper1.pdf
Geometric and Spatial Reasoning in BDI Agents:
a Survey
Angelo Ferrando1,∗,† , Andrea Gatti2,∗,† and Viviana Mascardi2,∗,†
1
University of Modena-Reggio Emilia, Italy
2
University of Genova, Italy
Abstract
In this paper we survey the literature on geometric and spatial modelling and reasoning in Belief-Desire-
Intention (BDI) agents and in Logic-Programming based approaches. The motivation for this survey is
the VEsNA framework, where cognitive agents implemented using the Jason BDI language and immersed
in an unknown virtual environment must build a map of that environment and reason about it.
Keywords
Literature Review, Geometric Modelling, Spatial Reasoning, Belief-Desire-Intention, Logic Programming,
Prolog, Virtual Reality, VEsNA
1. Introduction
According to the well known definition by Jennings, Sycara and Wooldridge [1], an intelligent
agent is a computer system, situated in some environment, capable of autonomous, social,
reactive, and pro-active actions. The strong definition of agents adds to these characteristics
the requirement of being conceptualised in terms of mentalistic features like beliefs, desires,
goals, intentions, leading to the notion of cognitive agents. The most known architecture for
cognitive agents is the Belief-Desire-Intention (BDI) one [2].
We believe that, in the ChatGPT [3] and metaverse [4] era, agent’s sociality should involve
humans by design, and situatedness should take virtual reality into account. Hence, our vision
of the next generation intelligent software agents involves cognition and reasoning abilities,
natural language interaction, and virtual reality.
To make our vision concrete we designed and developed VEsNA [5], a general-purpose
and agent-based framework for managing Virtual Environments via Natural language Agents.
VEsNA is freely available to the research community from https://github.com/driacats/VEsNA,
and seamlessly integrates cognition, natural language, and virtual reality. Currently, we are
addressing the challenging research questions
• RQ1: how can VEsNA agents perceive the unknown virtual reality they are situated in?,
CILC 2024: 39th Italian Conference on Computational Logic, June 26-28, 2024, Rome, Italy
∗
Corresponding authors.
†
The authors contributed equally.
Envelope-Open angelo.ferrando@unimore.it (A. Ferrando); andrea.gatti@edu.unige.it (A. Gatti); viviana.mascardi@unige.it
(V. Mascardi)
Orcid 0000-0002-8711-4670 (A. Ferrando); 0009-0003-0992-4058 (A. Gatti); 0000-0002-2261-9926 (V. Mascardi)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
• RQ2: how can they create and maintain a geometric representation of their environment?,
• RQ3: how can they reason about spatial relations?.
Although preliminary and subject to improvements, we have an answer for RQ1. This paper
paves the way to answering questions RQ2 and RQ3 by presenting the results of a review
carried out on the relevant related literature. We do not aim at answering the two questions
above in this paper; rather, we aim at identifying the works where answers can be found.
The paper is organized in the following way. Section 2 provides the context for our research
by introducing VEsNA. Section 3 provides pointers to recent papers useful for answering RQ2,
while Section 4 presents the result of our Systematic Literature Review (SLR) on BDI agents and
spatial reasoning. To the best of our knowledge no such review, providing a partial answer to
RQ3, exists in the recent literature; for this reason, we addresses the analysis of the literature
in a systematic and reproducible way. Finally, Section 5 concludes.
2. VEsNA
Figure 1: VEsNA architecture.
VEsNA is a general-purpose and agent-based framework for managing Virtual Environments
via Natural language Agents. As shown in Figure 2, VEsNA integrates
(i) a chatbot-like interface to support the sociality of agents; thanks to the chatbot supporting
natural language interaction, sociality is not limited to agent-agent conversations, but it
is extended to agent-human ones;
(ii) a framework for building the dynamic virtual environment; virtual reality is very suitable
for supporting agents’ situatendness: therein, agents are expected to perceive the reality
and to act over it;
(iii) a framework for implementing cognitive, declarative agents able to reason about knowl-
edge (about what users say, about the environment, about themselves) and to provide
human-readable explanations; the cognitive framework supports the agents’ autonomy,
reactivity, proactivity.
In VEsNA’s first release, the chatbot was implemented in Dialogflow [6] and the virtual
environment in Unity [7]. In the current release, we substituted Dialogflow with Rasa [8, 9],
which is an open source project, and Unity with Godot [10], again fully open source and
lighter than Unity. In both releases the framework for cognitive agents was JaCaMo [11]. For
the implementation of BDI agents, JaCaMo exploits the Jason interpreter for the AgentSpeak
declarative language [12].
In our recent research, we addressed a few key questions related with the two boxes depicted
in gray in Figure 2, namely how to integrate and exploit ontologies in VEsNA, and how to
monitor interactions among agents and between agents and humans.
In [13] we described On2Conv that takes in input an ontology modelling the domain of
interest and generates both the skeleton for the Jason agent’s plans and the intents for the
Dialogflow chatbot, coherently with the provided ontological knowledge. On2Conv ensures
that the chatbot and the Jason agents in charge for managing messages from the chatbot are
aligned, since they are automatically created from the same ontology. The On2Conv extension,
meant at using ontologies at design time to drive the generation of code, is not available in the
VEsNA current release since it targets Dialogflow as the chatbot. We are currently porting it to
Rasa.
In [14], instead, we proposed RV4Rasa, a Runtime Verification framework that monitors
whether a given chatbot deviates from its expected behaviour. Its instantiation to monitor Rasa
chatbots is presented, and the integration in VEsNA is available.
Figure 2: Interacting with Unity through Dialogflow thanks to VEsNA.
The screenshot1 in Figure 2 shows a chatbot interface on the left, where the human user asks
to add an ABB IRB 2600 industrial robot on the left of a previously placed robot, a Yaskawa MA
20100. The request is parsed by the chatbot, is then passed to the cognitive agent coordinating
the VEsNA framework, and the agent creates and positions the new robot - that is just an object
- in the virtual environment shown on the right. Note that no autonomous intelligent agent is
inserted yet in that environment.
The challenge that we are facing now, is allowing VEsNA autonomous agents to be born in
the virtual reality, to create a map of the unknown, dynamic environment they are situated
in, and to behave accordingly based on some long term goals, specified in a declarative way.
1
The screenshot is taken from the first VEsNA release where we used Dialogflow and Unity.
1 var objects = sight.get_overlapping_bodies()
𝑜2 objects
𝑜1 𝑜1 𝑜2
𝑟2
𝑟1
2 Does 𝑟1 from 𝑎 to 𝑜1 intersect other objects? No
Does 𝑟2 from 𝑎 to 𝑜2 intersect other objects? Yes, 𝑜1
Only 𝑜1 is visible to the agent 𝑎 , 𝑜2 is occluded, sending:
𝑎
{perception: sight,
data: {object: 𝑜1 , rotation: up, distance: near}}
3 The message is received by the environment artifact and a signal is sent to the agent:
signal("seen", parseLiteral(obj), parseLiteral(rot), parseLiteral(dist);
4 The actor reacts to the signal, knows its actual position and adds a belief:
+seen(Object, Rotation, Distance)
: position(X, Y)
<- +saw(X, Y, Direction, Object, Distance).
Assuming the agent in position (0, 0) the new belief is:
saw(0, 0, up, o_1, near).
Figure 3: VEsNA’s answer to RQ1.
This requires to create a connection between the brain of the agents, implemented in Jason, and
their virtual body implemented in Godot. The connection must allow the agents to perceive the
environment and create, and timely update, a mental map of it.
The implementation of the way VEsNA agents see the environment and port what they see
into their brain consists of two interconnected components:
1. The physical view implemented on the Godot side;
2. The transition from perception to belief on the JaCaMo side.
As shown in Figure 2 the connection between the two is implemented using a WebSocket
connection. The agent instantiated in the virtual scene is equipped with a cylindrical collider,
namely viewcone similar to the one described by Aversa et al. [15], that moves by following
the head and intercepting objects that are then in front of the agent. With this first control,
however, the viewcone intercepts all objects in an area without considering possible occlusions
that make the object not visible to the agent. In order to take also occlusions into account,
we added a further raycast step: we create a vector from the agent’s head to the seen object
and check that there are indeed no other objects between the two. In this case Godot sends
a message to JaCaMo containing the name of the seen object, the rotation of the agent at the
moment of perception and the distance at which it is located.
A JaCaMo environment artifact receives the message and sends a signal to the agent with
Figure 4: A VEsNA agent starting to explore its virtual environment.
the information. The agent intercepts the signal and creates a new belief where it stores the
position, rotation, distance and object seen.
In Figure 2 we show the results of our very preliminary experiments, where one agent sees a
wall and incorporates the belief that a wall is there, into its belief base.
This technical solution represents our current answer to RQ1. While we are able to represent
beliefs about the surrounding virtual environment, the representation is too limited and ad-hoc
to scale up to more realistic scenarios, and to be extended with meaningful semantic information.
This is where the review presented in the next sections to answer RQ2 and RQ3 comes into
play.
3. RQ2: Geometric Modelling in BDI agents
To review the literature on geometric modelling in BDI agents, we exploited Google Scholar
setting 2005–2024 as time range and relevance as ranking criterion. The query we submitted
to Google Scholar, ("geometry" OR "geometric modeling" OR "geometric modelling")
AND "BDI" , allowed us to expand our horizons far beyond computer science. We learned that
BDI may mean Boron–Diindomethene, which has interesting applications in bioanalytical
processes, but also Beck Depression Inventory and Blood Damage Index. In the domain of
insulators and superconductors, BDI is a chiral orthogonal class, whatever this means! Out
of the first 20 papers returned by the query above, only two used the acronym BDI as Belief-
Desire-Intention.
The first, by Antunes et al. [16], has nothing to do with geometric modelling; it deals with
the origin, adoption and evolution of desires in BDI architectures, and was returned because of
the title, “The Geometry of Desire”. The second, by YenChern et al. [17], describes a very simple
exercise of modelling a fire evacuation scenario using well known agent-oriented methodologies.
The authors claim that they implemented the scenario in Unity 3D using a BDI plugin, which
would be very interesting for our research, but the plugin is not explained and referenced in the
paper. Also this second paper does not answer RQ2 in any way.
The query ("geometry" OR "geometric modeling" OR "geometric modelling") AND
"Belief Desire Intention" returned results related with the BDI architecture as we intend
it, but still none provided us with useful hints on how to model the geometric features of a
3D environment in a generic BDI framework. Many of the returned papers [18, 19] deal with
integrating the BDI architecture with GAMA, an environment for implementing and running
“spatially explicit” multi-agent simulations [20]: they include the word geometry for this reason,
but the GAMA approach to support BDI agents cannot be easily ported to Jason.
In the Jason framework, beliefs are modelled as Prolog facts. We hence extended the query
to ("geometry" OR "geometric modeling" OR "geometric modelling") AND "prolog" .
This allowed us to retrieve a very recent paper [21] that overviews some notable Prolog tools for
automated reasoning in geometry, and provides details on ArgoTriCS, the Automated Triangle
Construction Solver [22].
We stopped our search here, as the survey included in [21] represents a good starting point
for answering RQ2, and our goal at this stage was to identify pointers to the relevant literature,
not the final answer.
Our impression however is that, even if we had continued our search and carried out a more
systematic review, the results would have been very limited. Integration of geometric modelling
features inside BDI agents has never been explored as a research problem on its own, but always
as parts of projects and experiments, leading to ad-hoc solutions. The study of the connections
between computational geometry and Prolog raised interest in the eighties and nineties of the
last century [23, 24, 25, 26], but it does not seem to be very fashionable nowadays.
For these reasons, we preferred to devote more time and effort to surveying works related
with spatial reasoning, as the results we obtained – presented in Section 4 – were more abundant
and promising.
4. RQ3: Spatial Reasoning in BDI Agents
The review of the literature on spatial reasoning in BDI agents was carried out in a
systematic way. We issued the four queries "spatial reasoning" AND "BDI" , "spatial
reasoning" AND "belief desire intention" , "spatial reasoning" AND "prolog" ,
Ref. Year Model. lang. Impl. lang. Other lang.
[27] 2008 None Unclear NetLogo+BDI+FIPA-ACL
[28] 2010 LP AgentSpeak Jadex, JTS Topol., PostGIS
[29, 30, 31] 2011-2015 CLP CLP(QS) (SWI-Prolog) C++, REDUCE
[32] 2011 Modal Logic Prolog-like NetLogo+BDI
[33] 2012 ASP GQR Perl, C++, Gringo
[34, 35] 2015-2017 ASPMT ASPMT(QS) None
[36] 2015 LP Unclear Unclear
[37] 2016 CLP CLP(QS) (SWI-Prolog) FreeCAD CGCS
[38, 39, 40] 2016-2018 LP Prolog-like NetLogo+BDI
[41] 2017 LP Prolog None
[42] 2017 LP SWI-Prolog C++
[43] 2018 ASP Clingo 5.2.0 None
[44] 2018 LP LogC-QSD (SWI-Prolog) None
[45] 2018 LP Prolog None
[46] 2021 LP SWI-Prolog Arcmap
[47] 2021 GeoJSON GeoJSON + 2P-Kt Prolog Tile38, Lattuce
[48] 2023 LP, CLP SWI-Prolog OpenCV, Tesseract OCR
Table 1
High level formalism for modelling spatial concepts (Model. lang.), language for implementing spatial
concepts (Impl. lang.), other languages and frameworks used for the implementation (Other lang.).
"spatial reasoning" AND "logic programming" to Google Scholar, setting the last 20
years as range for the results, and ranking by relevance. For each query, we analysed the first
10 results. We excluded books, technical reports, PhD theses, master theses (apart one that
was returned by multiple queries and was hence included because of its relevance) and papers
already retrieved with previous queries. We also excluded papers that presented methodological
approaches, with no implementation, unless they were part of a research stream by the same
group. After these search and filtering stages, 22 papers entered our review. They appear, listed
in chronological order, in Tables 1 and 2. Papers belonging to the same stream of research are
listed together in the same row.
Given that our goal is to equip our Jason agents with geometric and spatial reasoning
capabilities, the more relevant papers are those where BDI appears in column Agnt, in Table 2.
Although returned by our search, the work by Sakellariou et al. [27] deals with spatial
reasoning is a very marginal way. It describes the integration of BDI mechanisms in the
agent-based modelling and simulation tool NetLogo [54]. The integration is not available
to the community, and the notion of space is important only because it is important – by
design – in NetLogo, where turtles, namely mobile agents, are expected to move around to
accomplish their tasks. In NetLogo turtles are situated in a discrete torus-like 2D environment
(with 3D extensions not mentioned in the paper) and can perceive features of the squares
(patches) they are positioned in, and those around. In [27] spatial information is modelled as it
is normally modelled in NetLogo, with assertions like "fire-at [23 15]" . No spatial calculus
is adopted, and no support to temporal reasoning is provided. The case study is a simulation of
Ref. T. Calculus Agnt Case study A.
[27] No None BDI Forest fire scenario No
Autonomous, collision-free motion in dy-
[28] No RCC BDI No
namic environments in a logistic domain
[29], [30], 2D point-based spatial Geometric reasoning from Computer-
No No No
[31] calculi Aided Architecture Design
Behind, FrontOf,
[32] Yes BDI Simple example from traffic domain No
RightOf, LeftOf, On
Temporal reasoning formalisms, the Inter-
[33] Yes QCN No No
val Algebra and RCC
Non-monotonic spa- Ramification problem; geometric reason-
[34], [35] Yes No [49]
tial reasoning ing; frame problem; abductive reasoning
[36] Yes QTC, QMPC, RCC BDI Fleet of taxi scenario No
[37] No RCC No Circle contact; spiral chain; lamp layout No
[38], [39], Simple example with agents using electric
Yes RCC BDI [50]
[40] cars navigating to public charging point
Simple examples with manually encoded
[41] No RCC No No
data representing geographical data
Robot Simulated & implemented experiments
[42] No None No
swarm with multi-robot swarms
[43] Yes TC-6 and TC-10 No Fleet of taxi, T-Drive dataset [51]
[44] No QSD, QSD-Jux, PLCA No Spatial tests on the LogC-QSD-Dataset No
[45] No RCC No Layout of Multimedia documents No
Entity- and Relation-
[46] No No Analysis of accidents in Berlin [52]
Based Programming
Nearby, within, inter-
[47] No No None No
sect
Maths puzzles assigned to children of 4th
[48] No None No [53]
and 5th grade of primary school
Table 2
Support to temporal relations (T.), principal calculus used for spatial reasoning (Calculus), integration
of the spatial reasoning capabilities into intelligent agents or robots (Agnt.), Case study, availability of
the implemented reasoner/framework to the research community (A.).
a multi-agent system in a simple scenario that involves forest fires detection and suppression.
The work by [28] is inspired by the region connection calculus (RCC) for qualitative spatial
reasoning [55]. In particular, it uses RCC8 that consists of 8 basic binary relations possible
between two regions: disconnected (DC), externally connected (EC), equal (EQ), partially
overlapping (PO), tangential proper part (TPP), tangential proper part inverse (TPPi), non-
tangential proper part (NTPP), non-tangential proper part inverse (NTPPi). They define beliefs
and plans using the Jason syntax. As an example of plan exploiting spatial relations and
reasoning upon them, the avoid_collision plan looks like
+!avoid_collision(PA, A): true <-
detect_spatial_relations_for_movement(ID, PA, RMR, MA);
detect_spatial_relations_for_obstacle(ID, PA, ROR, OA);
reason_spatial_composition(ID, RMR, ROR,PA, MA, OA, ...);
-+collision(CA);
!move_to_position(PA, MA, OA, A).
Even without going into the technical details of the plan syntax, where some variables have
been omitted for readability, it is clear from the names of the actions appearing in the plan
body, that reasoning about space is implemented, suggesting that the ideas presented there
could be exploited by us. The MAS has been implemented in Jadex [56] and experiments have
been run on an autonomous, collision-free motion scenario in dynamic environments. The
logistics task of the autonomous resources (dumper) is to transport spoil automatically from
one point of the construction side to another point as an iterative action. Unfortunately, the
code is not available to the community: albeit very inspiring, we have not enough information
to integrate this approach in VEsNA.
Males and Zarnic [32] represent spatio-temporal knowledge using a directional orientation
approach and two out of eight relations Left and Front given by Freksa [57], plus the Meet
relation introduced in Ribaric and Hrkac [58]. They integrate this spatio-temporal knowledge
and the BDI modalities in a quantified modal logic. A simulation of an example in the traffic
domain is implemented in NetLogo extended with two libraries that implement BDI concepts.
How the gap between the modal logic and the NetLogo implementation is filled, is not explained,
and the NetLogo model is not available.
Vahidnia et al. [36] implemented a logic engine named SISMORA, driven by 91 rules
and axioms for the spatial and motion management. To give the flavor of how these rules
look like, we show one of them taken from the deduction system for motion relations:
In these relations, taken from the qualitative motion path calculus (QMPC) proposed by the
authors and based on the qualitative trajectory calculus (QTC [59]), M stands for moving, F
for following on, R for reachable. An object may also relatively move away from (MA) or
towards (MT) the other. For example, MR9 says that if x is following y on a motion path p
and y is moving towards z on p, then x is also moving towards z on p. Axioms and rules for
means-end reasoning have been proposed, and the BDI interpreter has been re-implemented
by the authors. These BDI agents are seamlessly integrated in an architecture where spatial
information is managed via a GIS, and experiments were run on a taxi fleet scenario. Although
very articulated, the lack of code and the extremely ad-hoc architecture suggest that we could
not easily reuse it for our scope.
Finally, in a sequence of papers published between 2016 and 2018, Othmane et al. [38, 39, 40]
describe a recommender system based on the BDI model, where a spatio-temporal represen-
tation of beliefs and desires is proposed. A spatio-temporal belief is an event defined as a
spatial relation holding in a temporal interval. The eight binary topological predicates of the
RCC calculus are generalized for modeling fuzzy topological relations, in order to represent
an imprecise spatio-temporal belief or desire such as “An accident occurred around 8 PM
between road A and road B” or“I want to be at work before 9 am”. An empirical evaluation of
the proposed model in the traffic domain has been addressed using NetLogo extended with
BDI libraires. Being implemented in NetLogo, although the code is available no Jason-like
representation of space and plans is provided, making this approach not adaptable to our needs.
Since only 7 papers deal with BDI agents and spatial reasoning, and they turned out to
propose approaches that cannot to be ported to VEsNA, we expanded our search to the
integration of spatial reasoning into Prolog and, more in general, Logic Programming. Below,
we briefly summarize the main findings of our analysis of all the 22 papers.
Table 1 classifies the 22 papers according to the high level formalisms and implementation
languages involved.
The 11 papers tagged with LP in the Model. lang. column use Logic Programming-style
facts and rules for representing spatial concepts and for reasoning on top of them. 3 of them,
directly use SWI-Prolog as implementation language. 1 uses AgentSpeak. In 6 cases, there was
no explicit reference to the Prolog (or “Prolog-like”) language used in the implementation, in
1 case the implementation language was hard to guess from the paper, even in general terms.
Finally, in one case LogC-QSD, a logical approach to compose qualitative shape descriptors
defined in the paper, was used. It is worth to notice that LogC-QSD is built on top of SWI-Prolog.
The 5 papers tagged with CLP adopt Constraint Logic Programming (one adopts both LP
and CLP). 4 of them use CLP(QS) as declarative reasoning framework. CLP(QS) was proposed
and then used by Bhatt et al. starting from 2011, and is capable of representing and reasoning
about high-level, qualitative spatial knowledge about the world. A CLP(QS) prototype has been
implemented through a loose integration in C++ between SWI-Prolog and REDUCE, a tool
dating back 2004 that does not seem to be available online.
Moving to the papers using less frequently used approaches, 2 papers use ASP, and only one
uses ASP Modulo Theory to represent spatial knowledge and implement the reasoning upon it.
The work by Forti [47] is different from the others: it is a masted thesis, and it aims at
integrating the GeoJSON format2 into the 2P-Kt Prolog interpreter3 . The result is named Geo2p,
and it extends 2P-Kt with predicates like intersects, within, nearby.
As far as Table 2 is concerned, the most significant finding for our research is the relevance
and wide adoption of the already mentioned RCC calculus used for spatial reasoning (column
Calculus). Besides RCC, other calculi include Qualitative Constraint Network (QCN [33] ),
which is a graph where the nodes denote the spatial or temporal variables, and the labels on
the directed edges between the nodes describe the possible relations between them; TC-6 and
TC-10, two qualitative calculi for trajectories defined based on arbitrarily partitioned maps
[43]; Qualitative Shape Descriptor (QSD [60]); the juxtaposition scheme QSD-Jux [61]; the
Point-Line-Circuit-Area (PLCA, [62]).
2
https://datatracker.ietf.org/doc/html/rfc7946, accessed on June 6, 2024.
3
https://github.com/tuProlog/2p-kt, accessed on June 6, 2024.
We may note that case studies cover very diverse domains, and that only few papers have
code associated with them (column A.).
To summarize the outcome of our SLR, no proposals for modelling and reasoning about space
and geometry are available for the Jason that we use in the VEsNA framework. The approach
closest to our needs [28] is described in AgentSpeak and implemented in Jadex, but the details
provided in the paper are too few and the code is not available. The other works that mention
BDI agents, either implement them in NetLogo – which is surprising, given the availability of
frameworks that implement BDI agents in a way more respectful of the original BDI conception
– or implement an ad-hoc BDI architecture. As far as the calculi are concerned, the survey
suggests to go into the details of RCC. RCC is modelled by some authors in Prolog-like languages,
that could be integrated into Jason.
5. Conclusions and Future Work
In this paper we have surveyed the literature on spatial and geometric reasoning in BDI agents
and, more in general, in logic-based frameworks. We found no solution ready to be ported into
VEsNA, but we obtained very interesting hints on the further literature to explore, and this is
the close-future work we plan to face.
While searching for answers to our research questions, we discovered a very interesting
literature field on semantic mapping, that we briefly explored starting from the paper by Chaves
, Ruiz-Sarmiento, Petkov and Jiménez [63], where ViMantic is presented. ViMantic is a semantic
mapping architecture for building and maintaining semantic maps of robot workspaces. A
semantic map, in the robotic context, is a models that includes information about spatial elements
(rooms, objects, etc.) augmented with the semantics – namely, what are the object for, what can
be done with them and what cannot be done, which are the states they can be, etc. – required
for an efficient robot operation [64, 65].
Although we are not dealing with robots, but with cognitive agents, the problems arising in
the robotic context are very similar to ours, disregarding the fact that perception and action
of VEsNA agents take place in the Virtual Reality, while robots are immersed in the real
environment. While this makes a significant difference, as discussed by Davis and Ramulu [42],
we are confident that sensory input received by VEsNA agents is comparable, at the right level
of abstraction, with the input received by robots, and hence the methods developed for semantic
mapping in robotics can be ported to VEsNA agents. Indeed, virtual environments are simpler
than the real one, so we are in an advantageous position.
Chaves et al. identify the following requirements for implementing a semantic mapping:
1. A well defined model representation.
2. A mechanism for automatically populating the model representation with information
coming from the environment.
3. Distributed execution.
4. Suitable human-robot interfaces.
5. Quality control.
6. Public availability.
The tool that they deem to be most suitable for representing the model, is an ontology. Chaves
et al.’s vision is very inspiring, because ontologies are a key element of the abstract VEsNA
architecture, although currently used at design time, and human-robot interfaces, that in the
case of intelligent agents should be human-agent interfaces, are also supported by VEsNA’s
chatbot front-end.
The exploration of the literature on semantic mapping is hence our medium-term future
work: we need to solve lower level representational problems before moving to semantics, but
that is the direction we head towards.
Acknowledgments
This work was partially supported by the “ENGINES — ENGineering INtElligent Systems around
intelligent agent technologies” project funded by the Italian MUR program “PRIN 2022” under
grant number 20229ZXBZM.
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