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Lavindra de Silva

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22 papers
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22

AAMAS Conference 2026 Conference Paper

HyperTensioN and Total-order Forward Decomposition Optimizations

  • Maurício Cecílio Magnaguagno
  • Felipe Meneguzzi
  • Lavindra de Silva

Hierarchical Task Network (HTN) planners generate plans using a decomposition process with extra domain knowledge to guide search towards achieving a task. Domain experts develop such domain knowledge through recipes of how to decompose higher level tasks and under what conditions. By leveraging a three-stage compiler design we can support more language descriptions and preprocessing optimizations to exploit such domain knowledge that when chained can greatly improve runtime efficiency. In this paper we evaluate such optimizations with the HyperTensioN HTN planner: winner of the HTN IPC 2020 total-order track.

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AAAI Conference 2026 Conference Paper

Hypertension and Total-Order Forward Decomposition Optimizations (Abstract Reprint)

  • Maurício Cecílio Magnaguagno
  • Felipe Meneguzzi
  • Lavindra de Silva

Hierarchical Task Network (HTN) planners generate plans using a decomposition process with extra domain knowledge to guide search towards a planning task. Domain experts develop such domain knowledge through recipes of how to decompose higher level tasks, specifying which tasks can be decomposed and under what conditions. In most realistic domains, such recipes contain recursions, i.e., tasks that can be decomposed into other tasks that contain the original task. Such domains require that either the domain expert tailor such domain knowledge to the specific HTN planning algorithm, or an algorithm that can search efficiently using such domain knowledge. By leveraging a three-stage compiler design we can easily support more language descriptions and preprocessing optimizations that when chained can greatly improve runtime efficiency in such domains. In this paper we evaluate such optimizations with the HyperTensioN HTN planner, winner of the HTN IPC 2020 total-order track.

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JAAMAS Journal 2025 Journal Article

Hypertension and total-order forward decomposition optimizations

  • Maurício Cecílio Magnaguagno
  • Felipe Meneguzzi
  • Lavindra de Silva

Abstract Hierarchical Task Network (HTN) planners generate plans using a decomposition process with extra domain knowledge to guide search towards a planning task. Domain experts develop such domain knowledge through recipes of how to decompose higher level tasks, specifying which tasks can be decomposed and under what conditions. In most realistic domains, such recipes contain recursions, i. e. , tasks that can be decomposed into other tasks that contain the original task. Such domains require that either the domain expert tailor such domain knowledge to the specific HTN planning algorithm, or an algorithm that can search efficiently using such domain knowledge. By leveraging a three-stage compiler design we can easily support more language descriptions and preprocessing optimizations that when chained can greatly improve runtime efficiency in such domains. In this paper we evaluate such optimizations with the HyperTensioN HTN planner, winner of the HTN IPC 2020 total-order track.

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EAAI Journal 2024 Journal Article

Bayesian dynamic modelling for probabilistic prediction of pavement condition

  • Yiming Zhang
  • Alix Marie d’Avigneau
  • Georgios M. Hadjidemetriou
  • Lavindra de Silva
  • Mark Girolami
  • Ioannis Brilakis

Significant funds have been allocated to maintain road networks each year in developed countries. Performance prediction is crucial for pavement management systems to adjust working plans and budget allocation. As a Bayesian nonparametric method, Gaussian process regression (GPR) is powerful in predicting nonlinear time series and quantifying uncertainty. However, it remains computationally intensive and fails to adapt to the time-varying characteristics. To address such issues, a dynamic GPR model is proposed for probabilistic prediction of the International Roughness Index (IRI) for flexible pavements. A moving window strategy is developed to substantially shrink the size of training data, which effectively alleviates computational cost and thus leads to a dynamic GPR. A genetic algorithm is then adopted to determine the optimal window size by considering the trade-off between computational efficiency and accuracy. A dataset acquired from Long-Term Pavement Performance (LTPP) is used to demonstrate the feasibility of the dynamic GPR. Its performance is compared to traditional GPR as well as dynamic and static Bayesian linear regression (BLR) models. The comparison results indicate that the proposed dynamic GPR can increase the accuracy by 0. 86, 1. 52, and 2. 27 times for dynamic BLR, static GPR, and static BLR, respectively. It exhibits the best results in terms of accuracy and uncertainty metrics due to its nonlinear modelling and time-varying ability.

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AAAI Conference 2020 Conference Paper

An Operational Semantics for True Concurrency in BDI Agent Systems

  • Lavindra de Silva

Agent programming languages have proved useful for formally modelling implemented systems such as PRS and JACK, and for reasoning about their behaviour. Over the past decades, many agent programming languages and extensions have been developed. A key feature in some of them is their support for the specification of ‘concurrent’ actions and programs. However, their notion of concurrency is still limited, as it amounts to a nondeterministic choice between (sequential) action interleavings. Thus, the notion does not represent ‘true concurrency’, which can more naturally exploit multicore computers and multi-robot manufacturing cells. This paper provides a true concurrency operational semantics for a BDI agent programming language, allowing actions to overlap in execution. We prove key properties of the semantics, relating to true concurrency and to its link with interleaving.

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IJCAI Conference 2020 Conference Paper

BDI Agent Architectures: A Survey

  • Lavindra de Silva
  • Felipe Meneguzzi
  • Brian Logan

The BDI model forms the basis of much of the research on symbolic models of agency and agent-oriented software engineering. While many variants of the basic BDI model have been proposed in the literature, there has been no systematic review of research on BDI agent architectures in over 10 years. In this paper, we survey the main approaches to each component of the BDI architecture, how these have been realised in agent programming languages, and discuss the trade-offs inherent in each approach.

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TCS Journal 2019 Journal Article

HTN-like solutions for classical planning problems: An application to BDI agent systems

  • Lavindra de Silva
  • Lin Padgham
  • Sebastian Sardina

In this paper we explore the question of what characterises a desirable plan of action and how such a plan could be computed, in the context of systems that already possess a certain amount of hierarchical domain knowledge. In contrast to past work in this setting, which focuses on generating low-level plans, losing much of the domain knowledge inherent in such systems, we argue that plans ought to be HTN-like or abstract, i. e. , re-use and respect the user-supplied know-how in the underlying domain. In doing so, we recognise an intrinsic tension between striving for abstract plans but ensuring that unnecessary actions, not linked to the specific goal to be achieved, are avoided. We explore this tension by characterising the set of “ideal” abstract plans that are non-redundant but maximally abstract, and then develop a more limited yet feasible account in which a given (arbitrary) abstract plan is “specialised” into one such non-redundant plan that is as abstract as possible. We present an algorithm that can compute such specialisations, and analyse the theoretical properties of our proposal.

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IJCAI Conference 2018 Conference Paper

An Operational Semantics for a Fragment of PRS

  • Lavindra de Silva
  • Felipe Meneguzzi
  • Brian Logan

The Procedural Reasoning System (PRS) is arguably the first implementation of the Belief--Desire--Intention (BDI) approach to agent programming. PRS remains extremely influential, directly or indirectly inspiring the development of subsequent BDI agent programming languages. However, perhaps surprisingly given its centrality in the BDI paradigm, PRS lacks a formal operational semantics, making it difficult to determine its expressive power relative to other agent programming languages. This paper takes a first step towards closing this gap, by giving a formal semantics for a significant fragment of PRS. We prove key properties of the semantics relating to PRS-specific programming constructs, and show that even the fragment of PRS we consider is strictly more expressive than the plan constructs found in typical BDI languages.

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AAMAS Conference 2018 Conference Paper

HTN Acting: A Formalism and an Algorithm

  • Lavindra de Silva

Hierarchical Task Network (HTN) planning is a practical and efficient approach to planning when the ‘standard operating procedures’ for a domain are available. Like Belief-Desire-Intention (BDI) agent reasoning, HTN planning performs hierarchical and context-based refinement of goals into subgoals and basic actions. However, while HTN planners ‘lookahead’ over the consequences of choosing one refinement over another, BDI agents interleave refinement with acting. There has been renewed interest in making HTN planners behave more like BDI agent systems, e. g. to have a unified representation for acting and planning. However, past work on the subject has remained informal or implementation-focused. This paper is a formal account of ‘HTN acting’, which supports interleaved deliberation, acting, and failure recovery. We use the syntax of the most general HTN planning formalism and build on its core semantics, and we provide an algorithm which combines our new formalism with the processing of exogenous events. We also study the properties of HTN acting and its relation to HTN planning.

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IJCAI Conference 2017 Conference Paper

Process Plan Controllers for Non-Deterministic Manufacturing Systems

  • Paolo Felli
  • Lavindra de Silva
  • Brian Logan
  • Svetan Ratchev

Determining the most appropriate means of producing a given product, i. e. , which manufacturing and assembly tasks need to be performed in which order and how, is termed process planning. In process planning, abstract manufacturing tasks in a process recipe are matched to available manufacturing resources, e. g. , CNC machines and robots, to give an executable process plan. A process plan controller then delegates each operation in the plan to specific manufacturing resources. In this paper we present an approach to the automated computation of process plans and process plan controllers. We extend previous work to support both non-deterministic (i. e. , partially controllable) resources, and to allow operations to be performed in parallel on the same part. We show how implicit fairness assumptions can be captured in this setting, and how this impacts the definition of process plans.

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ECAI Conference 2016 Conference Paper

Realisability of Production Recipes

  • Lavindra de Silva
  • Paolo Felli
  • Jack C. Chaplin
  • Brian Logan 0001
  • David Sanderson
  • Svetan M. Ratchev

There is a rising demand for customised products with a high degree of complexity. To meet these demands, manufacturing lines are increasingly becoming autonomous, networked, and intelligent, with production lines being virtualised into a manufacturing cloud, and advertised either internally to a company, or externally in a public cloud. In this paper, we present a novel approach to two key problems in such future manufacturing systems: the realisability problem (whether a product can be manufactured by a set of manufacturing resources) and the control problem (how a particular product should be manufactured). We show how both production recipes specifying the steps necessary to manufacture a particular product, and manufacturing resources and their topology can be formalised as labelled transition systems, and define a novel simulation relation which captures what it means for a recipe to be realisable on a production topology. We show how a controller that can orchestrate the resources in order to manufacture the product on the topology can be extracted from the simulation relation, and give an algorithm to compute a simulation relation and a controller.

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ECAI Conference 2016 Conference Paper

Summary Information for Reasoning About Hierarchical Plans

  • Lavindra de Silva
  • Sebastian Sardiña
  • Lin Padgham

Hierarchically structured agent plans are important for efficient planning and acting, and they also serve (among other things) to produce "richer" classical plans, composed not just of a sequence of primitive actions, but also "abstract" ones representing the supplied hierarchies. A crucial step for this and other approaches is deriving precondition and effect "summaries" from a given plan hierarchy. This paper provides mechanisms to do this for more pragmatic and conventional hierarchies than in the past. To this end, we formally define the notion of a precondition and an effect for a hierarchical plan; we present data structures and algorithms for automatically deriving this information; and we analyse the properties of the presented algorithms. We conclude the paper by detailing how our algorithms may be used together with a classical planner in order to obtain abstract plans.

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IROS Conference 2015 Conference Paper

The HATP hierarchical planner: Formalisation and an initial study of its usability and practicality

  • Lavindra de Silva
  • Raphaël Lallement
  • Rachid Alami 0001

HTN planners have generally relied on specialised languages for domain and problem representations. To facilitate adoption by other communities such as robotics, however, and integration with real world applications written in standard programming languages, we need HTN planners that are based on more familiar concepts from structured programming, and that come ready with features supporting integration. In this paper, we demonstrate how the HATP (Hierarchical Agent-based Task Planner) HTN planner offers such “syntactic sugar” and some of these features. Moreover, since it has a conceptually distinct syntax compared to traditional HTN planners, we also develop a formalism to unambiguously capture HATP's syntax and an important subset of its semantics, which we then use to compare against the formalism of a well understood family of HTN planners and to show that the former is sound. Finally, we demonstrate that despite quite possibly using “heavier” data structures to naturally capture HATP's syntax/semantics, and thereby facilitate extensions to HATP and integration with other applications, the implementation still performs acceptably.

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ICRA Conference 2014 Conference Paper

A new approach to combined symbolic-geometric backtracking in the context of human-robot interaction

  • Lavindra de Silva
  • Mamoun Gharbi
  • Amit Kumar Pandey
  • Rachid Alami 0001

Bridging the gap between symbolic and geometric planning has received much attention in recent years. An important issue in some of the works that combine the two approaches is finding the right balance between backtracking at the symbolic level versus at the geometric planning level. We present in this work a new approach to interleaved backtracking, where the symbolic planner backtracks to try alternative action branches that naturally map to different geometric solutions. This eliminates the need to “protect” certain symbolic conditions when backtracking at the geometric level, and addresses a completeness issue in our previous approach to interleaved backtracking. We discuss a concrete, non-trivial symbolic-geometric planning example in the context of Human-Robot Interaction, a full implementation of the combined planning technique, and an evaluation of performance as well as the effect of increasing the symbolic-action branching factor.

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IROS Conference 2013 Conference Paper

An interface for interleaved symbolic-geometric planning and backtracking

  • Lavindra de Silva
  • Amit Kumar Pandey
  • Rachid Alami 0001

While symbolic planners work with an abstract representation of the real world, allowing plans to be constructed relatively quickly, geometric planning - although more computationally complex - is essential for building symbolic plans that actually work in the real world. To combine the two types of systems, we present in this paper a meaningful interface, and insights into a methodology for developing interwoven symbolic-geometric domains. We concretely present this “link” between the two approaches with algorithms and data structures that amount to an intermediate layer that coordinates symbolic-geometric planning. Since both planners are capable of “backtracking” at their own levels, we also investigate the issue of how to interleave their backtracking, which we do in the context of the algorithms that form the link. Finally, we present a prototype implementation of the combined system on a PR2 robot.

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KER Journal 2013 Journal Article

Planning in BDI agents: a survey of the integration of planning algorithms and agent reasoning

  • Felipe Meneguzzi
  • Lavindra de Silva

Abstract Agent programming languages have often avoided the use of automated (first principles or hierarchical) planners in favour of predefined plan/recipe libraries for computational efficiency reasons. This allows for very efficient agent reasoning cycles, but limits the autonomy and flexibility of the resulting agents, oftentimes with deleterious effects on the agent's performance. Planning agents can, for instance, synthesise a new plan to achieve a goal for which no predefined recipe worked, or plan to make viable the precondition of a recipe belonging to a goal being pursued. Recent work on integrating automated planning with belief-desire-intention (BDI)-style agent architectures has yielded a number of systems and programming languages that exploit the efficiency of standard BDI reasoning, as well as the flexibility of generating new recipes at runtime. In this paper, we survey these efforts and point out directions for future work.

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AAMAS Conference 2009 Conference Paper

First Principles Planning in BDI Systems

  • Lavindra de Silva
  • Sebastian Sardina
  • Lin Padgham

BDI (Belief, Desire, Intention) agent systems are very powerful, but they lack the ability to incorporate planning. There has been some previous work to incorporate planning within such systems. However, this has either focussed on producing low-level plan sequences, losing much of the domain knowledge inherent in BDI systems, or has been limited to HTN (Hierarchical Task Network) planning, which cannot find plans other than those specified by the programmer. In this work, we incorporate classical planning into a BDI agent, but in a way that respects and makes use of the procedural domain knowledge available, by producing abstract plans that can be executed using such knowledge. In doing so, we recognize an intrinsic tension between striving for abstract plans and, at the same time, ensuring that unnecessary actions, unrelated to the specific goal to be achieved, are avoided. We explore this tension, by first characterizing the set of “ideal” abstract plans that are non-redundant while maximally abstract, and then developing a more limited but feasible account in which an abstract plan is “specialized” into a new abstract plan that is non-redundant and preserves abstraction as much as possible. We describe an algorithm to compute such a plan specialization, as well as algorithms for the production of a valid high level plan, by deriving abstract planning operators from the BDI program.

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