Through the PAL program, researchers will develop software that will function as an "enduring personalized cognitive assistant" to help decision-makers manage their complex worlds of multiple simultaneous tasks and unexpected events.The DARPA grant was divided over Carnegie Mellon University (CMU) and Stanford Research Institute (SRI).
The CMU project was dubbed RADAR, for Reflective Agents with Distributed Adaptive Reasoning. The system's eventual goal was "The system will help busy managers to cope with time-consuming tasks such as organizing their E-mail, planning meetings, allocating scarce resources such as office space, maintaining a web site, and writing quarterly reports. Like any good assistant, RADAR must learn by interacting with its human master and by accepting explicit advice and instruction." [PAL1]
The SRI project was named CALO (Cognitive Agent that Learns and Organizes). It involved contributions from 20 additional research institutions. Its goal was broader than RADAR's. "The name was inspired by the Latin word “calonis,” which means “soldier’s assistant.” The CALO software, which will learn by working with and being advised by its users, will handle a broad range of interrelated decision-making tasks that have in the past been resistant to automation. It will have the capability to engage in and carry out routine tasks, and to assist when the unexpected happens." [PAL1]
This article is just a short introduction to CALO. Most of its information is derived from SRI's PAL website [PAL2]. The website emphasizes the learning aspect of the framework. Here, I focus on the relation between the components.
[PAL2] SRI's PAL website
CALO
To the user CALO consists of several applications. These applications however, are integrated into a common knowledge base, and governed by an autonomous agent that aims to assist the user in common tasks.
The following image from [CALO1] is an overview of the cognitive nature of CALO.
Machine learning ("learning in the wild") was an essential part of each CALO component. It had to be able to learn all kinds of new information without adding lines of code.
These are some of the types of learning strategies involved:
Different CALO components used different ontologies, but all were designed around a core Component Library (CLib) "The CLib is encoded in the Knowledge Machine knowledge representation language, with a subset being automatically transformable into the popular Web Ontology Language (OWL) of the Semantic Web framework." [CALO-MA3]
[CALO1] Building an Intelligent Personal Assistant (2006)
The following image from [CALO1] is an overview of the cognitive nature of CALO.
Machine learning ("learning in the wild") was an essential part of each CALO component. It had to be able to learn all kinds of new information without adding lines of code.
These are some of the types of learning strategies involved:
- learning by instruction (Tailor)
- learning by discussion (PLOW)
- learning by demonstration (LAPDOG)
- learning by observation (Markov Logic Nets)
Different CALO components used different ontologies, but all were designed around a core Component Library (CLib) "The CLib is encoded in the Knowledge Machine knowledge representation language, with a subset being automatically transformable into the popular Web Ontology Language (OWL) of the Semantic Web framework." [CALO-MA3]
[CALO1] Building an Intelligent Personal Assistant (2006)
Application Suite
CALO grew into of a suite of applications that groups around three centers:
- PExA, the Project Execution Assistant
- CALO-MA, the Meeting Assistant
- IRIS, the Information Assistant
From a cognitive agent perspective, PExA is the central component.
PExA - Project Execution Assistant
"PExA focuses on two key areas: time management and task management. Time management refers to the process of helping a user manage actual and potential temporal commitments. Time management critically involves meeting or appointment scheduling but further includes reminder generation and workload balancing. Task management involves the planning, execution, and oversight of tasks. Such tasks may be personal in that they originate with the user, or they may derive from responsibilities associated with a project." [PEXA2]
PExA contains several tools that allow it to learn new procedures, that can be used by SPARK, the task manager.
PExA includes the following applications
PExA contains several tools that allow it to learn new procedures, that can be used by SPARK, the task manager.
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| PExA Architecture |
PExA includes the following applications
- SPARK, the Task Manager
- PTIME, the Time Manager
- EMMA, the Event Management Assistant (with PTIME)
- EMP, the Execution Monitor and Predictor (ProPL)
- Towel, the Task Management / ToDo UI
- BEAM, the ToDo Interpreter
- SEAR, for State Estimation
- LEPT, the Duration Learner
- Query Manager
- ICEE, the Task Explainer
- Machinetta, the Team Coordinator
- Tailor, a Procedure Learner
- EzBuilder, a Procedure Learner
- LAPDOG, a Procedure Learner
- PrimTL, a Procedure Learner
- PLOW, a Procedure Learner
Each user had its own CALO. Machinetta took care of inter-CALO communication. [CALO1]
CALO-MA - Meeting Assistant
"The meeting assistant is designed to enhance a user’s participation in a meeting through mechanisms that track the topics that are discussed, the participants’ positions, and resultant decisions." [PEXA2]
"Our efforts are focused on assisting artifact producing meetings, i.e. meetings for which the intended outcome is a tangible product such as a project management plan or a budget." [CALO-MA1]
The Meeting Assisant used the Multimodal Discourse Ontology (MMD).
CALO-MA includes the following applications
"Our efforts are focused on assisting artifact producing meetings, i.e. meetings for which the intended outcome is a tangible product such as a project management plan or a budget." [CALO-MA1]
The Meeting Assisant used the Multimodal Discourse Ontology (MMD).
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| The meeting assistant architecture |
CALO-MA includes the following applications
- Dialog Manager
- Meeting Browser
- 2-D Drawing Recognizer
- 3-D Gesture Recognizer
[CALO-MA1] Multi-Human Dialogue Understanding for Assisting Artifact-Producing Meetings (2004)
[CALO-MA2] The CALO Meeting Speech Recognition and Understanding System (2008)
[CALO-MA3] Ontology-Based Discourse Understanding for a Persistent Meeting Assistant (2005)
[CALO-MA2] The CALO Meeting Speech Recognition and Understanding System (2008)
[CALO-MA3] Ontology-Based Discourse Understanding for a Persistent Meeting Assistant (2005)
IRIS - Information Assistant
"IRIS is an application framework for enabling users to create a “personal map” across their office-related information objects. IRIS is an acronym for "Integrate. Relate. Infer. Share."" [IRIS1]
IRIS collects raw data from several other applications on the user's desktop, and integrates this data into it's own knowledge base, using CLib based ontologies.
It contains a plugin framework and plugins for the following applications had been created:
IRIS collects raw data from several other applications on the user's desktop, and integrates this data into it's own knowledge base, using CLib based ontologies.
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| The three layer IRIS integration framework |
It contains a plugin framework and plugins for the following applications had been created:
- Email (Mozilla)
- Browser (Mozilla)
- Calendar (OpenOffice Glow)
- Chat (Jabber)
- File Explorer (custom made)
- Data editor/viewer (Personal Radar)
Adam Cheyer was the main contributor to IRIS. He left the CALO project in 2007 to cofound the CALO spin-off Siri. [SIRI1]
In 2007 IRIS, a Java application, was rewritten as a Windows application and renamed to CALO Express. [IRIS4]
CALO Express further contains the applications:
In 2007 IRIS, a Java application, was rewritten as a Windows application and renamed to CALO Express. [IRIS4]
CALO Express further contains the applications:
- Presentation Assistant (for quick creation of presentations)
- PrepPak (find, gather, and store relevant resources for common office tasks such as attending an upcoming meeting)
[IRIS1] IRIS: Integrate. Relate. Infer. Share. (2005)
[IRIS2] A Case Study in Engineering a Knowledge Base for an Intelligent Personal Assistant
(2006)
[IRIS3] Extracting Knowledge about Users’ Activities from Raw Workstation Contents (2006)
[IRIS4] Adam Cheyer personal website
[SIRI1] SIRI RISING: The Inside Story Of Siri’s Origins (2013)
"At the heart of CALO’s ability to act is a Task Manager that initiates, tracks, and executes activities and commitments on behalf of its user, while remaining responsive to external events. The Task Manager component of CALO is based on a reactive execution system called SPARK" [SPARK2]
"There is a need for agent systems that can scale to real-world applications, yet retain the clean semantic underpinning of more formal agent frameworks. We describe the SRI Procedural Agent Realization Kit (SPARK), a new BDI agent framework that combines these two qualities." [SPARK1]
SPARK is a BDI (Belief-Desire-Intention) agent. Its central function is described as "Each agent maintains a knowledge base (KB) of beliefs about the world and itself that is updated both by sensory input from the external world and by internal events. The agent has a library of procedures that provide declarative representations of activities for responding to events and for decomposing complex tasks into simpler tasks. At any given time the agent has a set of intentions, which are procedure instances that it is currently executing. The hierarchical decomposition of tasks bottoms out in primitive actions that instruct effectors to bring about some change in the outside world or the internal state of the agent. At SPARK’s core is the executor whose role is to manage the execution of intentions. It does this by repeatedly selecting one of the current intentions to process and performing a single step of that intention. Steps generally involve activities such as performing tests on and changing the KB, adding tasks, decomposing tasks by applying procedures, or executing primitive actions." [SPARK1]
[SPARK1] The SPARK Agent Framework (2004)
[SPARK2] Task Management under Change and Uncertainty Constraint Solving Experience with the CALO Project (2005)
[SPARK3] SPARK website
[SPARK4] Balancing Formal and Practical Concerns in Agent Design (2004)
[SPARK5] Introduction to SPARK (2004)
[IRIS2] A Case Study in Engineering a Knowledge Base for an Intelligent Personal Assistant
(2006)
[IRIS3] Extracting Knowledge about Users’ Activities from Raw Workstation Contents (2006)
[IRIS4] Adam Cheyer personal website
[SIRI1] SIRI RISING: The Inside Story Of Siri’s Origins (2013)
SPARK
SPARK is the central component of PExA."At the heart of CALO’s ability to act is a Task Manager that initiates, tracks, and executes activities and commitments on behalf of its user, while remaining responsive to external events. The Task Manager component of CALO is based on a reactive execution system called SPARK" [SPARK2]
"There is a need for agent systems that can scale to real-world applications, yet retain the clean semantic underpinning of more formal agent frameworks. We describe the SRI Procedural Agent Realization Kit (SPARK), a new BDI agent framework that combines these two qualities." [SPARK1]
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| SPARK Architecture |
SPARK is a BDI (Belief-Desire-Intention) agent. Its central function is described as "Each agent maintains a knowledge base (KB) of beliefs about the world and itself that is updated both by sensory input from the external world and by internal events. The agent has a library of procedures that provide declarative representations of activities for responding to events and for decomposing complex tasks into simpler tasks. At any given time the agent has a set of intentions, which are procedure instances that it is currently executing. The hierarchical decomposition of tasks bottoms out in primitive actions that instruct effectors to bring about some change in the outside world or the internal state of the agent. At SPARK’s core is the executor whose role is to manage the execution of intentions. It does this by repeatedly selecting one of the current intentions to process and performing a single step of that intention. Steps generally involve activities such as performing tests on and changing the KB, adding tasks, decomposing tasks by applying procedures, or executing primitive actions." [SPARK1]
[SPARK1] The SPARK Agent Framework (2004)
[SPARK2] Task Management under Change and Uncertainty Constraint Solving Experience with the CALO Project (2005)
[SPARK3] SPARK website
[SPARK4] Balancing Formal and Practical Concerns in Agent Design (2004)
[SPARK5] Introduction to SPARK (2004)
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