Meneguzzi, Felipe


Classifying Norm Conflicts using Learned Semantic Representations

arXiv.org Artificial Intelligence

As natural language uses a diverse and often vague way to express ideas, identifying a norm conflict and its causes While most social norms are informal, they are often in contracts is a challenging task. An ever larger number of formalized by companies in contracts to regulate contracts being currently generated necessitates a fast and reliable trades of goods and services. When poorly process to identify norm conflicts. However, since such written, contracts may contain normative conflicts contracts are written in natural language, traditional revision resulting from opposing deontic meanings or contradict methods involve contract makers reading the contract and specifications. As contracts tend to be identifying conflicting points between norms. Such a method long and contain many norms, manually identifying requires huge human-effort and may not guarantee a revision such conflicts requires human-effort, which is that eliminates all conflicts. In response, we provide three time-consuming and error-prone. Automating such contributions towards automatically identifying and classifying task benefits contract makers increasing productivity potential conflicts between norms in contracts.


Robust Goal Recognition with Operator-Counting Heuristics

arXiv.org Artificial Intelligence

Goal recognition is the problem of inferring the correct Operator-counting heuristics provide a unifying framework goal towards which an agent executes a plan, for a variety of sources of information from planning heuristics given a set of goal hypotheses, a domain model, [Hoffmann that provide both an estimate ofet al., 2004] and a (possibly noisy) sample of the plan being the total cost of a goal from any given state and and indication executed. This is a key problem in both cooperative of the actual operators likely to be in such plans. This and competitive agent interactions and recent information proves to be effective at differentiating between approaches have produced fast and accurate goal goal hypotheses in goal recognition, as we empirically show recognition algorithms.


Landmark-Based Approaches for Goal Recognition as Planning

arXiv.org Artificial Intelligence

The task of recognizing goals and plans from missing and full observations can be done efficiently by using 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 and plan 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 substantially faster recognition time over such techniques.


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

arXiv.org Artificial Intelligence

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 plan execution trace. Such capability is very important in domains where multiple agents cooperate and delegate tasks among themselves, e.g. 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 an agent can use our technique to determine - by observing a trace - whether an agent 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.


LSTM-Based Goal Recognition in Latent Space

arXiv.org Artificial Intelligence

Approaches to goal recognition have progressively relaxed the requirements about the amount of domain knowledge and available observations, yielding accurate and efficient algorithms capable of recognizing goals. However, to recognize goals in raw data, recent approaches require either human engineered domain knowledge, or samples of behavior that account for almost all actions being observed to infer possible goals. This is clearly too strong a requirement for real-world applications of goal recognition, and we develop an approach that leverages advances in recurrent neural networks to perform goal recognition as a classification task, using encoded plan traces for training. We empirically evaluate our approach against the state-of-the-art in goal recognition with image-based domains, and discuss under which conditions our approach is superior to previous ones.


Heuristic Approaches for Goal Recognition in Incomplete Domain Models

arXiv.org Artificial Intelligence

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 \textit{incomplete} (and possibly incorrect) domain theories. We show the efficiency and accuracy of our approaches empirically against a large dataset of goal and plan recognition problems with incomplete domains.


Online Goal Recognition as Reasoning over Landmarks

AAAI Conferences

Online goal recognition is the problem of recognizing the goal of an agent based on an incomplete sequence of observations with as few observations as possible. Recognizing goals with minimal domain knowledge as an agent executes its plan requires efficient algorithms to sift through a large space of hypotheses. We develop an online approach to recognize goals in both continuous and discrete domains using a combination of goal mirroring and a generalized notion of landmarks adapted from the planning literature. Extensive experiments demonstrate the approach is more efficient and substantially more accurate than the state-of-the-art.


Goal Recognition in Incomplete STRIPS Domain Models

AAAI Conferences

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 a goal recognition technique capable of recognizing goals using incomplete (and possibly incorrect) domain theories as well as noisy observations. Such recognition needs to cope with a much larger space of plan hypotheses consistent with observations. We show the efficiency and accuracy of our approach empirically against a large dataset of goal recognition problems with incomplete domains.


Goal Recognition in Incomplete Domain Models

AAAI Conferences

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.


Forecasting Demand with Limited Information Using Gradient Tree Boosting

AAAI Conferences

Demand forecasting is an important challenge for industries seeking to optimize service quality and expenditures. Generating accurate forecasts is difficult because it depends on the quality of the data available to train predictive models, as well as on the model chosen for the task. We evaluate the approach on two datasets of varying complexity and compare the results with three machine learning algorithms. Results show our approach can outperform these approaches.