Collaborating Authors


Avaya rolls out a turnkey virtual agent


Zeus Kerravala is founder and principal analyst with ZK Research. He spent 10 years at Yankee Group and prior to that held a number of corporate IT positions. Cloud communications provider Avaya has announced an update to its Avaya OneCloud platform, in which its Virtual Agent is available as a ready-to-deploy, turnkey, configurable service. This would let customers quickly deploy an artificial intelligence (AI)-powered virtual agent that could be used immediately. This complements Avaya's current Google Dialogflow-based agent that requires developers to create a virtual agent from scratch. There has been an intense focus on customer experience (CX) improvement during the past few years, because CX is now the top brand differentiator outweighing price, product quality and all other factors.

Features of a smart city


A smart city is a city that uses technology to provide services and solve city problems. The main goals of a smart city are to improve policy efficiency, reduce waste and inconvenience, improve social and economic quality, and maximize social inclusion. Due to the breadth of technologies that have been implemented under the smart city label, it is difficult to distill a precise definition of a smart city. As the world's population continues to urbanize – by 2050, 66% of the world's population is expected to be urban – there is a global trend toward the creation of smart cities. This tendency not only causes many physical, social, behavioural, economic, and infrastructure issues, but it also creates many opportunities. launches low-code digital agents for swift deployment

#artificialintelligence, which offers automation across customer engagement, support and conversational commerce for enterprises, has announced the availability of pre-built Dynamic AI Agents for rapid deployment across a number of verticals. The agents are designed to connect conversations across voice, text and chat, in multiple languages. The agents, which will be available in's Agents are also available to enhance employee experience by automating HR processes like onboarding and training, and IT management services. We seem to stand on the brink of a working world in which everything is automated, both for employees and customers.

Different types of Real-world problems in Problem Solving approach


What are Informed Search Algorithms? What is an Uninformed Search Algorithm? What are the five components of Problem-Solving Agents? We do not share your email to any 3rd party companies! Be ready to learn AI and ML.

Different types of Toy problems in Problem-Solving approach


What are Informed Search Algorithms? What is an Uninformed Search Algorithm? What are the five components of Problem-Solving Agents? We do not share your email to any 3rd party companies! Be ready to learn AI and ML.

GitHub - google-research/recsim_ng: RecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems


RecSimNG is a scalable, modular, differentiable simulator implemented in Edward2 and TensorFlow. It offers: a powerful, general probabilistic programming language for agent-behavior specification; an XLA-based vectorized execution model for running simulations on accelerated hardware; and tools for probabilistic inference and latent-variable model learning, backed by automatic differentiation and tracing. We describe RecSim NG and illustrate how it can be used to create transparent, configurable, end-to-end models of a recommender ecosystem. Specifically, we present a collection of use cases that demonstrate how the functionality described above can help both researchers and practitioners easily develop and train novel algorithms for recommender systems. Please cite the paper if you use the code from this repository in your work. This is not an officially supported Google product.

Embracing AWKWARD! Real-time Adjustment of Reactive Planning Using Social Norms


This paper presents the AWKWARD agent architecture for the development of agents in Multi-Agent Systems. AWKWARD agents can have their plans re-configured in real time to align with social role requirements under changing environmental and social circumstances. The proposed hybrid architecture makes use of Behaviour Oriented Design (BOD) to develop agents with reactive planning and of the well-established OperA framework to provide organisational, social, and interaction definitions in order to validate and adjust agents' behaviours. Together, OperA and BOD can achieve real-time adjustment of agent plans for evolving social roles, while providing the additional benefit of transparency into the interactions that drive this behavioural change in individual agents. We present this architecture to motivate the bridging between traditional symbolic- and behaviour-based AI communities, where such combined solutions can help MAS researchers in their pursuit of building stronger, more robust intelligent agent teams.

Three Intelligent Agent State Representation in Artificial Intelligence


Each state of the world is a black box with no internal structure. Each state has a fixed set of variables or attributes that holds a value. We do not share your email to any 3rd party companies! Be ready to learn AI and ML. Save my name, email, and website in this browser for the next time I comment.

Agent-Based Modeling for Predicting Pedestrian Trajectories Around an Autonomous Vehicle

Journal of Artificial Intelligence Research

This paper addresses modeling and simulating pedestrian trajectories when interacting with an autonomous vehicle in a shared space. Most pedestrian–vehicle interaction models are not suitable for predicting individual trajectories. Data-driven models yield accurate predictions but lack generalizability to new scenarios, usually do not run in real time and produce results that are poorly explainable. Current expert models do not deal with the diversity of possible pedestrian interactions with the vehicle in a shared space and lack microscopic validation. We propose an expert pedestrian model that combines the social force model and a new decision model for anticipating pedestrian–vehicle interactions. The proposed model integrates different observed pedestrian behaviors, as well as the behaviors of the social groups of pedestrians, in diverse interaction scenarios with a car. We calibrate the model by fitting the parameters values on a training set. We validate the model and evaluate its predictive potential through qualitative and quantitative comparisons with ground truth trajectories. The proposed model reproduces observed behaviors that have not been replicated by the social force model and outperforms the social force model at predicting pedestrian behavior around the vehicle on the used dataset. The model generates explainable and real-time trajectory predictions. Additional evaluation on a new dataset shows that the model generalizes well to new scenarios and can be applied to an autonomous vehicle embedded prediction.

What Artificial Intelligence Still Can't Do


Modern artificial intelligence is capable of wonders. It can produce breathtaking original content: poetry, prose, images, music, human faces. Last year it produced a solution to the "protein folding problem," a grand challenge in biology that has stumped researchers for half a century. Yet today's AI still has fundamental limitations. Relative to what we would expect from a truly intelligent agent--relative to that original inspiration and benchmark for artificial intelligence, human cognition--AI has a long way to go. Critics like to point to these shortcomings as evidence that the pursuit of artificial intelligence is misguided or has failed. The better way to view them, though, is as inspiration: as an inventory of the challenges that will be important to address in order to advance the state of the art in AI.