Ramon Fraga Pereira

AAMAS Conference 2025 Conference Paper

Generalised BDI Planning

• Felipe Meneguzzi
• Ramon Fraga Pereira
• Nir Oren

Agent interpreters based on the Beliefs, Desires, and Intentions (BDI) model traditionally perform means-ends reasoning using plan libraries composed of reactive planning rules. However, the design of such rules often imposes a heavy knowledge engineering burden on a designer, and trades off flexibility for runtime efficiency. This use of planning rules originates from the limitations of planning technology at the time of the first BDI implementations. While these limitations have gradually been overcome by the integration of various types of planning into existing BDI theories, the corresponding interpreters remain fundamentally plan-library based. In this paper, we develop a novel BDI agent architecture driven by generalised planning as means-ends reasoning, in a radical departure from existing architectures. This architecture has two key properties. First, it more closely resembles the foundations of BDI logic and reasoning. Second, it offers substantial gains in efficiency in comparison with an architecture driven by classical planning.

AAMAS Conference 2025 Conference Paper

Intention Recognition in Real-Time Interactive Navigation Maps

• Peijie Zhao
• Zunayed Arefin
• Felipe Meneguzzi
• Ramon Fraga Pereira

In this demonstration, we develop IntentRec4Maps, a system to recognise users’ intentions in real-time interactive navigation maps. IntentRec4Maps uses the Google Maps Platform as the realworld interactive map, and a well-known approach for recognising intentions in real-time. We showcase IntentRec4Maps using two different Path-Planners and a Large Language Model (LLM).

AAMAS Conference 2025 Conference Paper

Uncertain Machine Ethics Planning

• Simon Kolker
• Louise A. Dennis
• Ramon Fraga Pereira
• Mengwei Xu

Machine Ethics decisions should consider the implications of uncertainty over decisions. Decisions should be made over sequences of actions to reach preferable outcomes long term. The evaluation of outcomes, however, may invoke one or more moral theories, which might have conflicting judgements. Each theory will require differing representations of the ethical situation. For example, Utilitarianism measures numerical values, Deontology analyses duties, and Virtue Ethics emphasises moral character. While balancing potentially conflicting moral considerations, decisions may need to be made, for example, to achieve morally neutral goals with minimal costs. In this paper, we formalise the problem as a Multi-Moral Markov Decision Process and a Multi-Moral Stochastic Shortest Path Problem. We develop a heuristic algorithm based on Multi-Objective AO*, utilising Sven-Ove Hansson’s Hypothetical Retrospection procedure for ethical reasoning under uncertainty. Our approach is validated by a case study from Machine Ethics literature: the problem of whether to steal insulin for someone who needs it.

ECAI Conference 2024 Conference Paper

A Practical Operational Semantics for Classical Planning in BDI Agents

• Mengwei Xu 0002
• Tom Lumley
• Ramon Fraga Pereira
• Felipe Meneguzzi

Implementations of the Belief-Desire-Intention (BDI) architecture have a long tradition in the development of autonomous agent systems. However, most practical implementations of the BDI framework rely on a pre-defined plan library for decision-making, which places a significant burden on programmers, and still yields systems that may be brittle, struggling to achieve their goals in dynamic environments. This paper overcomes this limitation by introducing an operational semantics for BDI systems that rely on Classical Planning at run time to both cope with failures that were unforeseeable and synthesise new plans that were unspecified at design time. This semantics places particular emphasis on the interaction of the reasoning cycle and an underlying planning algorithm. We empirically demonstrate the practical feasibility and generality of such an approach in an implementation of this semantics within two popular BDI platforms together with in-depth computational evaluation.

IJCAI Conference 2024 Conference Paper

A Survey on Model-Free Goal Recognition

• Leonardo Amado
• Sveta Paster Shainkopf
• Ramon Fraga Pereira
• Reuth Mirsky
• Felipe Meneguzzi

Goal Recognition is the task of inferring an agent's intentions from a set of observations. Existing recognition approaches have made considerable advances in domains such as human-robot interaction, intelligent tutoring systems, and surveillance. However, most approaches rely on explicit domain knowledge, often defined by a domain expert. Much recent research focus on mitigating the need for a domain expert while maintaining the ability to perform quality recognition, leading researchers to explore Model-Free Goal Recognition approaches. We comprehensively survey Model-Free Goal Recognition, and provide a perspective on the state-of-the-art approaches and their applications, showing recent advances. We categorize different approaches, introducing a taxonomy with a focus on their characteristics, strengths, weaknesses, and suitability for different scenarios. We compare the advances each approach made to the state-of-the-art and provide a direction for future research in Model-Free Goal Recognition.

AAMAS Conference 2024 Conference Paper

Empowering BDI Agents with Generalised Decision-Making

• Ramon Fraga Pereira
• Felipe Meneguzzi

While research on software agents has long focused on explicit agent communication, there is comparatively less effort on implicit communication between agents via recognising each other’s intentions and desires for understanding their decision-making reasoning process. Since most human communication is not explicit, we aim to outline a research agenda to help endow autonomous agents with analogous coordination capabilities. In this paper, we formalise a framework that empowers the decision-making process of BDI agents in adversarial and cooperative environments by casting them as generalised planners using Theory of Mind. Our formalisation uses the fundamental philosophical properties of the BDI model and its reasoning process to outline a broad research agenda in agents’ research.

AAAI Conference 2024 Conference Paper

Generalising Planning Environment Redesign

• Alberto Pozanco
• Ramon Fraga Pereira
• Daniel Borrajo

In Environment Design, one interested party seeks to affect another agent's decisions by applying changes to the environment. Most research on planning environment (re)design assumes the interested party's objective is to facilitate the recognition of goals and plans, and search over the space of environment modifications to find the minimal set of changes that simplify those tasks and optimise a particular metric. This search space is usually intractable, so existing approaches devise metric-dependent pruning techniques for performing search more efficiently. This results in approaches that are not able to generalise across different objectives and/or metrics. In this paper, we argue that the interested party could have objectives and metrics that are not necessarily related to recognising agents' goals or plans. Thus, to generalise the task of Planning Environment Redesign, we develop a general environment redesign approach that is metric-agnostic and leverages recent research on top-quality planning to efficiently redesign planning environments according to any interested party's objective and metric. Experiments over a set of environment redesign benchmarks show that our general approach outperforms existing approaches when using well-known metrics, such as facilitating the recognition of goals, as well as its effectiveness when solving environment redesign tasks that optimise a novel set of different metrics.

AAMAS Conference 2023 Conference Paper

Domain-Independent Deceptive Planning

• Adrian Price
• Ramon Fraga Pereira
• Peta Masters
• Mor Vered

We investigate deceptive planning, the problem of generating a plan such that an observer is unable to determine its ultimate goal. Most work in this area has focused on path and/or motion planning. However planning problems can be quite varied and challenging. We present domain-independent approaches for deceptive plan generation utilising the concepts of landmarks, centroids, and minimum covering states. We introduce new, domain-independent metrics to evaluate a plan’s deceptivity as a ratio between its deceptive quantity and cost; and we extensively evaluate the performance of our proposed approaches over widely different planning domains providing guidelines as to when to use each approach.

AAAI Conference 2023 Conference Paper

Robust Neuro-Symbolic Goal and Plan Recognition

• Leonardo Amado
• Ramon Fraga Pereira
• Felipe Meneguzzi

Goal Recognition is the task of discerning the intended goal of an agent given a sequence of observations, whereas Plan Recognition consists of identifying the plan to achieve such intended goal. Regardless of the underlying techniques, most recognition approaches are directly affected by the quality of the available observations. In this paper, we develop neuro-symbolic recognition approaches that can combine learning and planning techniques, compensating for noise and missing observations using prior data. We evaluate our approaches in standard human-designed planning domains as well as domain models automatically learned from real-world data. Empirical experimentation shows that our approaches reliably infer goals and compute correct plans in the experimental datasets. An ablation study shows that outperform approaches that rely exclusively on the domain model, or exclusively on machine learning in problems with both noisy observations and low observability.

ECAI Conference 2023 Conference Paper

Scaling-Up LAO* in FOND Planning: An Ablation Study

• Ramon Fraga Pereira

The use of multi-queue heuristic search and tie-breaking strategies has shown to be very effective for satisficing planning in the Classical Planning setting. However, to the best of our knowledge, the use of such techniques has never been studied and employed in heuristic search algorithms for Fully Observable Non-Deterministic (FOND) Planning. In this paper, we adapt existing satisficing techniques for scaling-up an AND/OR heuristic search algorithm for FOND Planning. Namely, we employ multi-queue heuristic search, dead-end detection, and tie-breaking strategies in LAO* for improving the extraction of strong-cyclic policies. We assess the efficiency of our techniques in LAO* through an extensive ablation study over two different FOND Planning benchmarks. Empirical results show that our techniques improve the performance of LAO* in terms of coverage, expanded nodes, and planning time compared to a well-known planner based on vanilla LAO*. Indeed, the best configuration of our techniques is competitive with the current state-of-the-art in FOND Planning.

ICAPS Conference 2022 Conference Paper

Iterative Depth-First Search for FOND Planning

• Ramon Fraga Pereira
• André Grahl Pereira
• Frederico Messa
• Giuseppe De Giacomo

Fully Observable Non-Deterministic (FOND) planning models uncertainty through actions with non-deterministic effects. Existing FOND planning algorithms are effective and employ a wide range of techniques. However, most of the existing algorithms are not robust for dealing with both non-determinism and task size. In this paper, we develop a novel iterative depth-first search algorithm that solves FOND planning tasks and produces strong cyclic policies. Our algorithm is explicitly designed for FOND planning, addressing more directly the non-deterministic aspect of FOND planning, and it also exploits the benefits of heuristic functions to make the algorithm more effective during the iterative searching process. We compare our proposed algorithm to well-known FOND planners, and show that it has robust performance over several distinct types of FOND domains considering different metrics.

IJCAI Conference 2021 Conference Paper

A Survey on Goal Recognition as Planning

• Felipe Meneguzzi
• Ramon Fraga Pereira

Goal Recognition is the task of inferring an agent's goal, from a set of hypotheses, given a model of the environment dynamic, and a sequence of observations of such agent's behavior. While research on this problem gathered momentum as an offshoot of plan recognition, recent research has established it as a major subject of research on its own, leading to numerous new approaches that both expand the expressivity of domains in which to perform goal recognition and substantial advances to the state-of-the-art on established domain types. In this survey, we focus on the advances to goal recognition achieved in the last decade, categorizing the resulting techniques and identifying a number of opportunities for further breakthrough research.

AAAI Conference 2021 Conference Paper

An LP-Based Approach for Goal Recognition as Planning

• Luísa R. A. Santos
• Felipe Meneguzzi
• Ramon Fraga Pereira
• André Grahl Pereira

Goal recognition aims to recognize the set of candidate goals that are compatible with the observed behavior of an agent. In this paper, we develop a method based on the operatorcounting framework that efficiently computes solutions that satisfy the observations and uses the information generated to solve goal recognition tasks. Our method reasons explicitly about both partial and noisy observations: estimating uncertainty for the former, and satisfying observations given the unreliability of the sensor for the latter. We evaluate our approach empirically over a large data set, analyzing its components on how each can impact the quality of the solutions. In general, our approach is superior to previous methods in terms of agreement ratio, accuracy, and spread. Finally, our approach paves the way for new research on combinatorial optimization to solve goal recognition tasks.

AAMAS Conference 2021 Conference Paper

Combining LSTMs and Symbolic Approaches for Robust Plan Recognition

• Leonardo Rosa Amado
• Ramon Fraga Pereira
• Felipe Meneguzzi

AIJ Journal 2020 Journal Article

Landmark-based approaches for goal recognition as planning

• Ramon Fraga Pereira
• Nir Oren
• Felipe Meneguzzi

Recognizing goals and plans from complete or partial observations can be efficiently achieved through automated planning techniques. In many applications, it is important to recognize goals and plans not only accurately, but also quickly. To address this challenge, we develop novel goal recognition approaches based on planning techniques that rely on planning landmarks. In automated planning, landmarks are properties (or actions) that cannot be avoided to achieve a goal. We show the applicability of a number of planning techniques with an emphasis on landmarks for goal recognition tasks in two settings: (1) we use the concept of landmarks to develop goal recognition heuristics; and (2) we develop a landmark-based filtering method to refine existing planning-based goal and plan recognition approaches. These recognition approaches are empirically evaluated in experiments over several classical planning domains. We show that our goal recognition approaches yield not only accuracy comparable to (and often higher than) other state-of-the-art techniques, but also result in substantially faster recognition time over existing techniques.

TIST Journal 2020 Journal Article

Using Sub-Optimal Plan Detection to Identify Commitment Abandonment in Discrete Environments

• Ramon Fraga Pereira
• Nir Oren
• Felipe Meneguzzi

Assessing whether an agent has abandoned a goal or is actively pursuing it is important when multiple agents are trying to achieve joint goals, or when agents commit to achieving goals for each other. Making such a determination for a single goal by observing only plan traces is not trivial, as agents often deviate from optimal plans for various reasons, including the pursuit of multiple goals or the inability to act optimally. In this article, we develop an approach based on domain independent heuristics from automated planning, landmarks, and fact partitions to identify sub-optimal action steps—with respect to a plan—within a fully observable plan execution trace. Such capability is very important in domains where multiple agents cooperate and delegate tasks among themselves, such as through social commitments, and need to ensure that a delegating agent can infer whether or not another agent is actually progressing towards a delegated task. We demonstrate how a creditor can use our technique to determine—by observing a trace—whether a debtor is honouring a commitment. We empirically show, for a number of representative domains, that our approach infers sub-optimal action steps with very high accuracy and detects commitment abandonment in nearly all cases.

ICAPS Conference 2019 Conference Paper

Landmark-Enhanced Heuristics for Goal Recognition in Incomplete Domain Models

• Ramon Fraga Pereira
• André Grahl Pereira
• Felipe Meneguzzi

Recent approaches to goal recognition have progressively relaxed the assumptions about the amount and correctness of domain knowledge and available observations, yielding accurate and efficient algorithms. These approaches, however, assume completeness and correctness of the domain theory against which their algorithms match observations: this is too strong for most real-world domains. In this paper, we develop goal recognition techniques that are capable of recognizing goals using incomplete domain theories by considering different notions of planning landmarks in such domains. We evaluate the resulting techniques empirically in a large dataset of incomplete domains, and perform an ablation study to understand their effect on recognition performance.

IJCAI Conference 2019 Conference Paper

Online Probabilistic Goal Recognition over Nominal Models

• Ramon Fraga Pereira
• Mor Vered
• Felipe Meneguzzi
• Miquel Ramírez

This paper revisits probabilistic, model-based goal recognition to study the implications of the use of nominal models to estimate the posterior probability distribution over a finite set of hypothetical goals. Existing model-based approaches rely on expert knowledge to produce symbolic descriptions of the dynamic constraints domain objects are subject to, and these are assumed to produce correct predictions. We abandon this assumption to consider the use of nominal models that are learnt from observations on transitions of systems with unknown dynamics. Leveraging existing work on the acquisition of domain models via learning for Hybrid Planning we adapt and evaluate existing goal recognition approaches to analyze how prediction errors, inherent to system dynamics identification and model learning techniques have an impact over recognition error rates.

AAMAS Conference 2018 Conference Paper

Towards Online Goal Recognition Combining Goal Mirroring and Landmarks

• Mor Vered
• Ramon Fraga Pereira
• Maur�cio C. Magnaguagno
• Gal A. Kaminka
• Felipe Meneguzzi

AAMAS Conference 2017 Conference Paper

Detecting Commitment Abandonment by Monitoring Sub-Optimal Steps during Plan Execution

• Ramon Fraga Pereira
• Nir Oren
• Felipe Meneguzzi

ECAI Conference 2016 Conference Paper

Landmark-Based Plan Recognition

• Ramon Fraga Pereira
• Felipe Meneguzzi