The paper explores the use of Deep Reinforcement Learning (DRL) in stock market trading, focusing on two algorithms: Double Deep Q-Network (DDQN) and Proximal Policy Optimization (PPO) and compares them with Buy and Hold benchmark. It evaluates these algorithms across three currency pairs, the S&P 500 index and Bitcoin, on the daily data in the period of 2019-2023. The results demonstrate DRL's effectiveness in trading and its ability to manage risk by strategically avoiding trades in unfavorable conditions, providing a substantial edge over classical approaches, based on supervised learning in terms of risk-adjusted returns.

Artefacts compromise clinical decision-making in the use of medical time series. Pulsatile waveforms offer probabilities for accurate artefact detection, yet most approaches rely on supervised manners and overlook patient-level distribution shifts. To address these issues, we introduce a generalised label-free framework, GenClean, for real-time artefact cleaning and leverage an in-house dataset of 180,000 ten-second arterial blood pressure (ABP) samples for training. We first investigate patient-level generalisation, demonstrating robust performances under both intra- and inter-patient distribution shifts. We further validate its effectiveness through challenging cross-disease cohort experiments on the MIMIC-III database. Additionally, we extend our method to photoplethysmography (PPG), highlighting its applicability to diverse medical pulsatile signals. Finally, its integration into ICM+, a clinical research monitoring software, confirms the real-time feasibility of our framework, emphasising its practical utility in continuous physiological monitoring. This work provides a foundational step toward precision medicine in improving the reliability of high-resolution medical time series analysis

Journal of Arti ial In telligen e Resear h 20 (2003) 139-144 Submitted 09/03; published 12/03 Commentary PDDL 2.1: Represen tation vs. Computation H e tor Gener he tor.geffner ICREA { Universitat Pomp eu F abr a Pase o de Cir unvala ion 8 08003 Bar elona, Sp ain Abstra t I ommen t on the PDDL 2.1 language and its use in the planning omp etition, fo using on the hoi es made for a ommo dating time and on urren y . I also dis uss some metho d-ologi al issues that ha v e to do with the mo v e to w ard more expressiv e planning languages and the balan e needed in planning resear h b et w een seman ti s and omputation. In tro du tion F o x and Long should b e thank ed and ongratulated for their eort in organizing and running the 3rd In ternational Planning Comp etition. They ame up with an extended planning language along with a n um b er of new problems and domains that hallenged existing planners and will ertainly hallenge future planners as w ell.

Many known planning tasks have inherent constraints concerning the best order in which to achieve the goals. A number of research efforts have been made to detect such constraints and to use them for guiding search, in the hope of speeding up the planning process. We go beyond the previous approaches by considering ordering constraints not only over the (top-level) goals, but also over the sub-goals that will necessarily arise during planning. Landmarks are facts that must be true at some point in every valid solution plan. We extend Koehler and Hoffmann's definition of reasonable orders between top level goals to the more general case of landmarks. We show how landmarks can be found, how their reasonable orders can be approximated, and how this information can be used to decompose a given planning task into several smaller sub-tasks. Our methodology is completely domain- and planner-independent. The implementation demonstrates that the approach can yield significant runtime performance improvements when used as a control loop around state-of-the-art sub-optimal planning systems, as exemplified by FF and LPG.

The on tin uous hidden v ariables denote the temp o. Ex-a t omputation of p osterior features su h as the MAP state is in tra table in this mo del lass, so w e in tro du e Mon te Carlo metho ds for in tegration and optimization. The metho ds an b e applied in b oth online and bat h s enarios su h as temp o tra king and trans ription and are th us p oten tially useful in a n um b er of m usi appli ations su h as adaptiv e automati a ompanimen t, s ore t yp esetting and m usi information retriev al. 1. Ho w ev er, when op erating on sampled audio data from p olyphoni a ousti al signals, extra tion of a s ore-lik e des ription is a v ery hallenging auditory s ene analysis task (V er o e, Gardner, & S heirer, 1998). In this pap er, w e fo us on a subproblem in m usi -ir, where w e assume that exa t timing information of notes is a v ailable, for example as a stream of MIDI 1 ev en ts from a digital k eyb oard. One example is automati s ore t yp esetting, 1. Musi al Instrumen ts Digital In terfa e. Ea h time a k ey is pressed, a MIDI k eyb oard generates a short message on taining pit h and k ey v elo it y . In on v en tional m usi notation, the onset time of ea h note is impli itly represen ted b y the um ulativ e sum of durations of previous notes. Durations are en o ded b y simple rational n um b ers (e.g., quarter note, eigh th note), onsequen tly all ev en ts in m usi are pla ed on a dis rete grid. This is due to the fa t that m usi ians in tro du e in ten tional (and unin ten tional) deviations from a me hani al pres ription. F or example timing of ev en ts an b e delib erately dela y ed or pushed. Moreo v er, the temp o an u tuate b y slo wing do wn or a elerating. In fa t, su h deviations are natural asp e ts of expressiv e p erforman e; in the absen e of these, m usi tends to sound rather dull and me hani al. On the other hand, if these deviations are not a oun ted for during trans ription, resulting s ores ha v e often v ery p o or qualit y . Robust and fast quan tization and temp o tra king is also an imp ortan t requiremen t for in tera tiv e p erforman e systems; appli ations that \listen" to a p erformer for generating an a ompanimen t or impro visation in real time (Raphael, 2001b; Thom, 2000). A t last, su h mo dels are also useful in m usi ology for systemati study and hara terization of expressiv e timing b y prin ipled analysis of existing p erforman e data. F rom a theoreti al p ersp e tiv e, sim ultaneous quan tization and temp o tra king is a \ hi k en-and-egg" problem: the quan tization dep ends up on the in tended temp o in terpre-tation and the temp o in terpretation dep ends up on the quan tization. Apparen tly, h uman listeners an resolv e this am biguit y (in most ases) without an y eort.

Despite the significant progress in multiagent teamwork, existing research does not address the optimality of its prescriptions nor the complexity of the teamwork problem. Without a characterization of the optimality-complexity tradeoffs, it is impossible to determine whether the assumptions and approximations made by a particular theory gain enough efficiency to justify the losses in overall performance. To provide a tool for use by multiagent researchers in evaluating this tradeoff, we present a unified framework, the COMmunicative Multiagent Team Decision Problem (COM-MTDP). The COM-MTDP model combines and extends existing multiagent theories, such as decentralized partially observable Markov decision processes and economic team theory. In addition to their generality of representation, COM-MTDPs also support the analysis of both the optimality of team performance and the computational complexity of the agents' decision problem. In analyzing complexity, we present a breakdown of the computational complexity of constructing optimal teams under various classes of problem domains, along the dimensions of observability and communication cost. In analyzing optimality, we exploit the COM-MTDP's ability to encode existing teamwork theories and models to encode two instantiations of joint intentions theory taken from the literature. Furthermore, the COM-MTDP model provides a basis for the development of novel team coordination algorithms. We derive a domain-independent criterion for optimal communication and provide a comparative analysis of the two joint intentions instantiations with respect to this optimal policy. We have implemented a reusable, domain-independent software package based on COM-MTDPs to analyze teamwork coordination strategies, and we demonstrate its use by encoding and evaluating the two joint intentions strategies within an example domain.

Simple conceptual graphs are considered as the kernel of most knowledge representation formalisms built upon Sowa's model. Reasoning in this model can be expressed by a graph homomorphism called projection, whose semantics is usually given in terms of positive, conjunctive, existential FOL. We present here a family of extensions of this model, based on rules and constraints, keeping graph homomorphism as the basic operation. We focus on the formal definitions of the different models obtained, including their operational semantics and relationships with FOL, and we analyze the decidability and complexity of the associated problems (consistency and deduction). As soon as rules are involved in reasonings, these problems are not decidable, but we exhibit a condition under which they fall in the polynomial hierarchy. These results extend and complete the ones already published by the authors. Moreover we systematically study the complexity of some particular cases obtained by restricting the form of constraints and/or rules.

In this paper, we present a method for recognising an agent's behaviour in dynamic, noisy, uncertain domains, and across multiple levels of abstraction. We term this problem on-line plan recognition under uncertainty and view it generally as probabilistic inference on the stochastic process representing the execution of the agent's plan. Our contributions in this paper are twofold. In terms of probabilistic inference, we introduce the Abstract Hidden Markov Model (AHMM), a novel type of stochastic processes, provide its dynamic Bayesian network (DBN) structure and analyse the properties of this network. We then describe an application of the Rao-Blackwellised Particle Filter to the AHMM which allows us to construct an efficient, hybrid inference method for this model. In terms of plan recognition, we propose a novel plan recognition framework based on the AHMM as the plan execution model. The Rao-Blackwellised hybrid inference for AHMM can take advantage of the independence properties inherent in a model of plan execution, leading to an algorithm for online probabilistic plan recognition that scales well with the number of levels in the plan hierarchy. This illustrates that while stochastic models for plan execution can be complex, they exhibit special structures which, if exploited, can lead to efficient plan recognition algorithms. We demonstrate the usefulness of the AHMM framework via a behaviour recognition system in a complex spatial environment using distributed video surveillance data.

The se s tructur es were embod ie d into a comprehensive agent be havi oral model shaped on t op of a unifying framework. By means of s uch a fr amework we managed to make the agent p la tform trans pare nt to the us er and, in the same time, deco uple the re us able patterns from the under lying mobile agent pl atfo rm. It thus beco mes cl ear that the model was s tructur ed to be highly indepe nd ent, encompas sing a handful of abst ract featur es that a llo w it to be eq ually expres sive re gardle s s of th e underlying agent suppor t . Enti ties common to eve ry agent p la tfor m (location, agent, mes s age, behavior, agent identifier along with ot her relevant ones) provi d e the cont ext within which we were able to d efine the reus ab le patterns . The s e patterns prod uc e an environment that ultimately sep arate s the behavi oral model from the a ctual s keleton upon which the pat ters are enacted (i.e. the J ADE agent plat fo rm) and, as s uch, once they are c re ated, rewriting them will not be necessary for every new p la tfor m. Simply put, one has onl y to write new ada p te rs if needed, or us e the avail able ones a long with the alread y exi s ting framewo rk items to integrate (coale sce) the compon ent sh e req ui res. Adapters were employed t o p rovi de the bridge between the framework and agent p la tfor ms .

Instantaneously Trained Neural Networks Abhilash Ponnath Abstract: This paper presents a review of instantaneously trained neural networks (ITNNs). These networks trade learning time for size and, in the basic model, a new hidden node is created for each training sample. Various versions of the corner-classification family of ITNNs, which have f ound applications in artificial intelligence (AI), are described. Implementation issues are also considered. 1 Introduction The human brain, the most complex known living structure in the universe, has the nerve cell or neuron as its fundamental unit. The number of neurons and connections between the neurons is enormous; this ensemble enables the brain to surpass the computational capacity of supercomputers in existence today. Artificial neural networks (ANNs) are models of the brain, which implement the mapping, ƒ: X Y such that the task is completed in a "certain" sense.