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.

In natural language processing (NLP), generating semantically-rich representations of sentences can improve performance on multiple tasks, such as question answering, duplicate detection, sentiment analysis, and machine translation. Recent approaches to NLP using machine learning can produce text representations that carry syntactic and semantic information. This article surveys recent works on generating sentence representations for semantic textual similarity tasks. We conduct our survey using a systematic literature review approach. We retrieve papers from several digital libraries and summarize their key techniques and findings. We propose a taxonomy to facilitate the understanding of the semantic textual similarity task on the sentence level. In our analysis, we describe the current state-of-the-art in sentence representation for semantic textual similarity and propose a guideline for working on this task. • Identification of the main research on semantic similarity between sentences. • Taxonomy to define the field of semantic similarity. • Identification of state-of-the-art approaches for existing datasets. • Guidelines for working on semantic similarity between sentences.

Building conversational agents to help humans in domain-specific tasks is challenging since the agent needs to understand the natural language and act over it while accessing domain expert knowledge. Modern natural language processing techniques led to an expansion of conversational agents, with recent pretrained language models achieving increasingly accurate language recognition results using ever-larger open datasets. However, the black-box nature of such pretrained language models obscures the agent’s reasoning and its motivations when responding, leading to unexplained dialogues. In this work, we develop a Belief-desire-intention (BDI) agent as a task-oriented dialogue system to introduce mental attitudes similar to humans describing their behavior during a dialogue. We compare the BDI model with pipeline task-oriented dialogue system architecture by leveraging existing components from dialogue systems and developing the agent’s intention selection as a dialogue policy. We show that combining traditional agent modeling approaches, such as BDI, with more recent learning techniques can result in efficient and scrutable dialogue systems.

In Hierarchical Task Network (HTN) planning, landmarks are facts that must hold true, and tasks or methods that must be included in every solution. Existing landmark generation techniques for HTN planning rely on the Delete and Ordering Free (DOF) relaxation and are known to be sound but incomplete, primarily due to the limitations introduced by Task Insertion. This paper presents a new landmark generation method that builds on a previous AND/OR graph-based approach, extending it to capture additional hierarchical dependencies among tasks and methods. We prove that our approach is sound and dominates existing techniques, though it remains incomplete under the DOF relaxation. Experimental results on IPC benchmarks for totally ordered problems show that our method identifies significantly more task and method landmarks across most domains, improving coverage with minimal computational overhead.

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.

Some imitation learning methods combine behavioural cloning with self-supervision to infer actions from state pairs. However, most rely on a large number of expert trajectories to increase generalisation and human intervention to capture key aspects of the problem, such as domain constraints. In this paper, we propose Continuous Imitation Learning from Observation (CILO), a new method augmenting imitation learning with two important features: (i) exploration, allowing for more diverse state transitions, requiring less expert trajectories and resulting in fewer training iterations; and (ii) path signatures, allowing for automatic encoding of constraints, through the creation of non-parametric representations of agents and expert trajectories. We compared CILO with a baseline and two leading imitation learning methods in five environments. It had the best overall performance of all methods in all environments, outperforming the expert in two of them.

While recent work on online goal recognition efficiently infers goals under low observability, comparatively less work focuses on online goal recognition that works in both discrete and continuous domains. Online goal recognition approaches often rely on repeated calls to the planner at each new observation, incurring high computational costs. Recognizing goals online in continuous space quickly and reliably is critical for any trajectory planning problem since the real physical world is fast-moving, e.g. robot applications. We develop an efficient method for goal recognition that relies either on a single call to the planner for each possible goal in discrete domains or a simplified motion model that reduces the computational burden in continuous ones. The resulting approach performs the online component of recognition orders of magnitude faster than the current state of the art, making it the first online method effectively usable for robotics applications that require sub-second recognition.

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.

Abstract Goal Recognitionis the task of discerning the intended goal that an agent aims to achieve, given a set of goal hypotheses, a domain model, and a sequence of observations (i.e., a sample of the plan executed in the environment). Existing approaches assume that goal hypotheses comprise a single conjunctive formula over a single final state and that the environment dynamics are deterministic, preventing the recognition of temporally extended goals in more complex settings. In this paper, we expand goal recognition totemporally extended goalsinFully Observable Non-Deterministic(fond) planning domain models, focusing on goals on finite traces expressed inLinear Temporal Logic(ltl $$_f$$ f ) andPure-Past Linear Temporal Logic(ppltl). We develop the first approach capable of recognizing goals in such settings and evaluate it using differentltl $$_f$$ f andppltlgoals over sixfondplanning domain models. Empirical results show that our approach is accurate in recognizing temporally extended goals in different recognition settings.

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.

For an agent in a multi-agent environment, it is often beneficial to be able to predict what other agents will do next when deciding how to act. Previous work in multi-agent intention scheduling assumes a priori knowledge of the current goals of other agents. In this paper, we present a new approach to multi-agent intention scheduling in which an agent uses online goal recognition to identify the goals currently being pursued by other agents while acting in pursuit of its own goals. We show how online goal recognition can be incorporated into an MCTS-based intention scheduler, and evaluate our approach in a range of scenarios. The results demonstrate that our approach can rapidly recognise the goals of other agents even when they are pursuing multiple goals concurrently, and has similar performance to agents which know the goals of other agents a priori.

Behavioural cloning is an imitation learning technique that teaches an agent how to behave via expert demonstrations. Recent approaches use self-supervision of fully-observable unlabelled snapshots of the states to decode state pairs into actions. However, the iterative learning scheme employed by these techniques is prone to get trapped into bad local minima. Previous work uses goal-aware strategies to solve this issue. However, this requires manual intervention to verify whether an agent has reached its goal. We address this limitation by incorporating a discriminator into the original framework, offering two key advantages and directly solving a learning problem previous work had. First, it disposes of the manual intervention requirement. Second, it helps in learning by guiding function approximation based on the state transition of the expert's trajectories. Third, the discriminator solves a learning issue commonly present in the policy model, which is to sometimes perform a ‘no action’ within the environment until the agent finally halts.

Most approaches for goal recognition rely on specifications of the possible dynamics of the actor in the environment when pursuing a goal. These specifications suffer from two key issues. First, encoding these dynamics requires careful design by a domain expert, which is often not robust to noise at recognition time. Second, existing approaches often need costly real-time computations to reason about the likelihood of each potential goal. In this paper, we develop a framework that combines model-free reinforcement learning and goal recognition to alleviate the need for careful, manual domain design, and the need for costly online executions. This framework consists of two main stages: Offline learning of policies or utility functions for each potential goal, and online inference. We provide a first instance of this framework using tabular Q-learning for the learning stage, as well as three measures that can be used to perform the inference stage. The resulting instantiation achieves state-of-the-art performance against goal recognizers on standard evaluation domains and superior performance in noisy environments.

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 operator-counting 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.

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.

Recent approaches to goal recognition have progressively relaxed the requirements about the amount of domain knowledge and available observations, yielding accurate and efficient algorithms. These approaches, however, assume that there is a domain expert capable of building complete and correct domain knowledge to successfully recognize an agent's goal. This is too strong for most real-world applications. We overcome these limitations by combining goal recognition techniques from automated planning, and deep autoencoders to carry out unsupervised learning to generate domain theories from data streams and use the resulting domain theories to deal with incomplete and noisy observations. Moving forward, we aim to develop a new data-driven goal recognition technique that infers the domain model using the same set of observations used in recognition itself.

Hierarchical Task Networks (HTN) planning uses a decomposition process guided by domain knowledge to guide search towards a planning task. While many HTN planners allow calls to external processes (e.g. to a simulator interface) during the decomposition process, this is a computationally expensive process, so planner implementations often use such calls in an ad-hoc way using very specialized domain knowledge to limit the number of calls. Conversely, the classical planners that are capable of using external calls (often called semantic attachments) during planning are limited to generating a fixed number of ground operators at problem grounding time. We formalize Semantic Attachments for HTN planning using semi coroutines, allowing such procedurally defined predicates to link the planning process to custom unifications outside of the planner, such as numerical results from a robotics simulator. The resulting planner then uses such coroutines as part of its backtracking mechanism to search through parallel dimensions of the state-space (e.g. through numeric variables). We show empirically that our planner outperforms the state-of-the-art numeric planners in a number of domains using minimal extra domain knowledge.

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.

Goal recognition is the task of identifying the goal an observed agent is pursuing. The quality of its results depends on the quality of the observed information. In most goal recognition approaches, the accuracy significantly decreases in settings with missing observations. To mitigate this issue, we develop a learning model based on LSTMs, leveraging attention mechanisms, to enhance observed traces by predicting missing observations in goal recognition problems. We experiment using a dataset of goal recognition problems and apply the model to enhance the observation traces where missing. We evaluate the technique using a state-of-the-art goal recognizer in four different domains to compare the accuracy between the standard and the enhanced observation traces. Experimental evaluation shows that recurrent neural networks with self-attention mechanisms improve the accuracy metrics of state-of-the-art goal recognition techniques by an average of 60%.

Imitation from observation is a computational technique that teaches an agent on how to mimic the behavior of an expert by observing only the sequence of states from the expert demonstrations. Recent approaches learn the inverse dynamics of the environment and an imitation policy by interleaving epochs of both models while changing the demonstration data. However, such approaches often get stuck into sub-optimal solutions that are distant from the expert, limiting their imitation effectiveness. We address this problem with a novel approach that overcomes the problem of reaching bad local minima by exploring: (i) a self-attention mechanism that better captures global features of the states; and (ii) a sampling strategy that regulates the observations that are used for learning. We show empirically that our approach outperforms the state-of-the-art approaches in four different environments by a large margin.

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.

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.

Creating an optimal amount of indexes, taking into account query performance and database size remains a challenge.In theory, one can speed up query response by creating indexes on the most used columns, although causing slower data insertion and deletion, and requiring a much larger amount of memory for storing the indexing data, but in practice, it is very important to balance such a trade-off.This is not a trivial task that often requires action from the Database Administrator.We address this problem by introducing GADIS, A Genetic Algorithm for Database Index Selection, designed to automatically select the best configuration of indexes adaptable for any database schema.This method aims to find the fittest individuals for optimizing both query response time, and disk required for the indexed data.We evaluate the effectiveness of GADISthrough several experiments we developed based on a standard database benchmark, compare it to three baseline indexing strategies, and show that our approach consistently leads to a better resulting index configuration.

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 work, we develop a goal recognition technique capable of recognizing goals using incomplete (and possibly incorrect) domain theories.

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.

Contracts formally represent agreements between parties and often involve the exchange of goods and services. In contracts, norms define the expected behaviors from parties using deontic statements, such as obligations, permissions, and prohibitions. However, norms may conflict when two or more apply to the same context but have different deontic statements, such as a permission to delay payment being present in the same contract as an obligation to pay in a fixed deadline. Contracts with conflicting norms may be invalidated in whole or in part, making conflict identification a major concern in contract writing. Conflict identification in contracts by humans is a time-consuming and error-prone task that would greatly benefit from automated aid. In order to automate such identification, we introduce an approach to identify potential conflicts between norms in contracts written in natural language that compares a latent (vector) representation of norms using an implicit offset that encodes normative conflicts. Experimental evaluation shows that our approach is substantially more accurate than the existing state of the art in an open dataset.

Sentiment analysis is an important technique to interpret user opinion on products from text, for example, as shared in social media. Recent approaches using deep learning can accurately extract overall sentiment from large datasets. However, extracting sentiment from specific aspects of a product with small training datasets remains a challenge. The automatic classification of sentiments at aspect-level can provide more detailed feedbacks about product and service opinions avoiding manual verification. In this work, we develop two deep learning approaches to classify sentiment at aspect-level using small datasets.

Recent approaches to goal recognition have progressively relaxed the requirements about the amount of domain knowledge and available observations, yielding accurate and efficient algorithms. These approaches, however, assume that there is a domain expert capable of building complete and correct domain knowledge to successfully recognize an agent's goal. This is too strong for most real-world applications. We overcome these limitations by combining goal recognition techniques from automated planning, and deep autoencoders to carry out unsupervised learning to generate domain theories from data streams and use the resulting domain theories to deal with incomplete and noisy observations. We show the effectiveness of the technique in a number of domains and compare the recognition effectiveness of the autoencoded against hand-coded versions of these domains.

The task of measure semantic redundancy between sentences demands a thorough interpretation from the reader because phrase meaning may be ambiguous. Detecting semantic similarity is a difficult problem because natural language, besides ambiguity, offers almost infinite possibilities to express the same idea. This paper adapts a siamese neural network architecture trained to measure the semantic similarity between two sentences through metric learning. The resulting solution should help in writing more efficient and informative text.

This article addresses the challenge of planning coordinated activities for a set of autonomous agents, who coordinate according to social commitments among themselves. We develop a multi-agent plan in the form of a commitment protocol that allows the agents to coordinate in a flexible manner, retaining their autonomy in terms of the goals they adopt so long as their actions adhere to the commitments they have made. We consider an expressive first-order setting with probabilistic uncertainty over action outcomes. We contribute the first practical means to derive protocol enactments which maximise expected utility from the point of view of one agent. Our work makes two main contributions. First, we show how Hierarchical Task Network planning can be used to enact a previous semantics for commitment and goal alignment, and we extend that semantics in order to enact first-order commitment protocols. Second, supposing a cooperative setting, we introduce uncertainty in order to capture the reality that an agent does not know for certain that its partners will successfully act on their part of the commitment protocol. Altogether, we employ hierarchical planning techniques to check whether a commitment protocol can be enacted efficiently, and generate protocol enactments under a variety of conditions. The resulting protocol enactments can be optimised either for the expected reward or the probability of a successful execution of the protocol. We illustrate our approach on a real-world healthcare scenario.

Automated planning can be used to efficiently recognize goals and plans from partial or full observed action sequences. In this paper, we propose goal recognition heuristics that rely on information from planning landmarks - facts or actions that must occur if a plan is to achieve a goal when starting from some initial state. We develop two such heuristics: the first estimates goal completion by considering the ratio between achieved and extracted landmarks of a candidate goal, while the second takes into account how unique each landmark is among landmarks for all candidate goals. We empirically evaluate these heuristics over both standard goal/plan recognition problems, and a set of very large problems. We show that our heuristics can recognize goals more accurately, and run orders of magnitude faster, than the current state-of-the-art.

Activity recognition applications is growing in importance due to two key factors: first there is increased need for more human assistance and surveillance; and second, increased availability of datasets and improved image recognition algorithms have allowed effective recognition of more sophisticated activities. In this paper we develop an activity recognition approach to support visually impaired people that leverages these advances. Specifically, our approach uses a dataset of videos taken from the point of view of a guide-dog to train a convolutional neural-network to recognize the activities taking place around the camera and provide feedback to a visually impaired human user. Our experiments show that our trained models surpass the current state-of-the-art for identifying activities in the doc-centric activity dataset.

With the growth of video content produced by mobile cameras and surveillance systems, an increasing amount of data is becoming available and can be used for a variety of applications such as video surveillance, smart homes, smart cities, and in-home elder monitoring. Such applications focus in recognizing human activities in order to perform different tasks allowing the opportunity to support people in their different scenarios. In this paper we propose a deep neural architecture for kitchen human action recognition. This architecture contains an ensemble of convolutional neural networks connected through different fusion methods to predict the label of each action. Experiments show that our architecture achieves the novel state-of-the-art for identifying cooking actions in a well-known kitchen dataset.

Several advantages can be obtained by allowing multi-agent systems to easily access ontologies, for example, in scenarios where agents make their decisions based on knowledge provided by ontologies. Thus, this paper presents an infrastructure to allow the use of web ontologies in different agent-oriented platforms. The agents use this infrastructure layer as a tool for storing, accessing and querying domain-specific OWL ontologies. As a result, this layer allows an integration of agent platforms with semantic web data and ontologies. We exemplify in practice how agents, coded in one such platform, can use the proposed access layer to ontological reasoning engines, as well as which features can be obtained from it. We evaluated and compared performance and memory consumption of this semantic infrastructure against usual knowledge representation in agent programming.

When entering a system, an agent should be aware of the obligations and prohibitions (collectively norms) that affect it. Existing solutions to this norm identification problem make use of observations of either norm compliant, or norm violating, behaviour. Thus, they assume an extreme situation where norms are typically violated, or complied with. In this paper we propose a Bayesian approach to norm identification which operates by learning from both norm compliant and norm violating behaviour. We evaluate our approach's effectiveness empirically and compare its accuracy to existing approaches. By utilising both types of behaviour, we not only overcome a major limitation of such approaches, but also obtain improved performance over the state of the art, allowing norms to be learned with fewer observations.

Recognition of goals and plans using incomplete evidence from action execution can be done efficiently by using planning techniques. In many applications it is important to recognize goals and plans not only accurately, but also quickly. In this paper, we develop a heuristic approach for recognizing plans based on planning techniques that rely on ordering constraints to filter candidate goals from observations. These ordering constraints are called landmarks in the planning literature, which are facts or actions that cannot be avoided to achieve a goal. We show the applicability of planning landmarks in two settings: first, we use it directly to develop a heuristic-based plan recognition approach; second, we refine an existing planning-based plan recognition approach by pre-filtering its candidate goals. Our empirical evaluation shows that our approach is not only substantially more accurate than the state-of-the-art in all available datasets, it is also an order of magnitude faster.

In multiagent systems, agents might interfere with each other as a side-effect of their activities. One approach to coordinating these agents is to restrict their activities by means of social norms whose violation results in sanctions to violating agents. We formalize a normative system within a stochastic environment and norm enforcement follows a stochastic model in which stricter enforcement entails higher cost. Within this type of system, we provide an approach to analize the tradeoff between norm enforcement efficiency and its cost considering a population of norm-aware selfish agents.

In order to address the challenges of greener energy generation, new techniques need to be developed both to generate electricity with lower emissions and to optimize energy distribution and consumption. Smart grid techniques have been developed specifically to tackle this latter challenge. This paper aims to contribute in improving the efficiency of energy use within a single household by modeling appliances within it as a multiagent system (MAS). We model this system as a virtual organization that seeks to minimize energy consumption while reaching a tradeoff between user comfort, energy cost and limiting peak energy usage.

Commitments help model interactions in multiagent systems in a computationally realizable yet high-level manner without compromising the autonomy and heterogeneity of the member agents. Recent work shows how to combine commitments with goals and apply planning methods to enable agents to determine their actions. However, previous approaches to modeling commitments are confined to propositional representations, which limits their applicability in practical cases. We propose a first-order representation and reasoning technique that accommodates templatic commitments and goals that may be applied repeatedly with differing bindings for domain objects. Doing so not only leads to a more perspicuous modeling, but also supports many practical patterns.

As the semantic web expands, ontological data becomes distributed over a large network of data sources on the Web. Consequently, evaluating queries that aim to tap into this distributed semantic database necessitates the ability to consult multiple data sources efficiently. In this paper, we propose methods and heuristics to efficiently query distributed ontological data based on a series of properties of summarized data. In our approach, each source summarizes its data as another RDF graph, and relevant section of these summaries are merged and analyzed at query evaluation time. We show how the analysis of these summaries enables more efficient source selection, query pruning and transformation of expensive distributed joins into local joins.

In coalition operations, information from different sources belong to different organisations have to be gathered and aggregated. The information from these resources may not be consistent. Inconsistencies in the gathered information creates severe uncertainties that hinders the usefulness of the information. In this paper, we have propose a Subjective Logic based approach for modelling the trustworthiness of information sources within a specific context. This model is used to handle inconsistencies through filtering information from less trustworthy sources.

In this paper we describe a software assistant agent that can proactively assist human users situated in a time-constrained environment to perform normative reasoning-reasoning about prohibitions and obligations-so that the user can focus on her planning objectives. In order to provide proactive assistance, the agent must be able to 1) recognize the user's planned activities, 2) reason about potential needs of assistance associated with those predicted activities, and 3) plan to provide appropriate assistance suitable for newly identified user needs. To address these specific requirements, we develop an agent architecture that integrates user intention recognition, normative reasoning over a user's intention, and planning, execution and replanning for assistive actions. This paper presents the agent architecture and discusses practical applications of this approach.

Human users trying to plan and accomplish information-dependent goals in highly dynamic environments with prevalent uncertainty must consult various types of information sources in their decision-making processes while the information requirements change as they plan and re-plan. When the users must make time-critical decisions in information-intensive tasks they become cognitively overloaded not only by the planning activities but also by the information-gathering activities at various points in the planning process. We have developed the ANTicipatory Information and Planning Agent (ANTIPA) to manage information adaptively in order to mitigate user cognitive overload. To this end, the agent brings information to the user as a result of user requests but most crucially, it proactively predicts the user's prospective information needs by recognizing the user's plan; pre-fetches information that is likely to be used in the future; and offers the information when it is relevant to the current or future planning decisions. This paper introduces a fully implemented agent of the ANTIPA architecture using a decision-theoretic user model.

This paper proposes an extension of the XSL-FO standard which allows the specification of an unlimited number of arbitrarily shaped page regions. These extensions are built on top of XSL-FO 1.1 to enable flow content to be laid out into arbitrary shapes and allowing for page layouts currently available only to desktop publishing software. Such a proposal is expected to leverage XSL-FO towards usage as an enabling technology in the generation of content intended for personalized printing.

Recent advances in digital press technology have enabled the creation of high-quality personalized documents, with the potential of generating an entire batch of one-of-a-kind documents. Even though digital presses are capable of printing such document sets as fast as they would print regular press jobs, raster image processing might possibly be performed for every different page in the job. Such process demands a large computational effort and it is therefore interesting to gather repeated images that are used throughout all documents and rasterize them as few times as possible. Moreover, performing such process separately from document production in the publishing workflow allows optimization to be performed prior to final printing, thus allowing it to take press hardware specifics into account, and reducing the time taken for it to produce the final output. This paper describes techniques to perform this task using PPML as the document description language, as well as the main issues concerning this kind of document optimization. Several gathering policies are described along with explanatory examples. We also provide and discuss experimental data supporting the use of such strategie.